Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2.

The World Health Organization declared the COVID-19 epidemic a public health emergency of international concern on March 11th, 2020, and the pandemic is rapidly spreading worldwide. COVID-19 is caused by a novel coronavirus SARS-CoV-2, which enters human target cells via angiotensin converting enzyme 2 (ACE2). We used a number of bioinformatics tools to computationally characterize ACE2 by determining its cell-specific expression in trachea, lung, and small intestine, derive its putative functions, and predict transcriptional regulation. The small intestine expressed higher levels of ACE2 mRNA than any other organ. By immunohistochemistry, duodenum, kidney and testis showed strong signals, whereas the signal was weak in the respiratory tract. Single cell RNA-Seq data from trachea indicated positive signals along the respiratory tract in key protective cell types including club, goblet, proliferating, and ciliary epithelial cells; while in lung the ratio of ACE2-expressing cells was low in all cell types (<2.6%), but was highest in vascular endothelial and goblet cells. Gene ontology analysis suggested that, besides its classical role in the renin-angiotensin system, ACE2 may be functionally associated with angiogenesis/blood vessel morphogenesis. Using a novel tool for the prediction of transcription factor binding sites we identified several putative binding sites within two tissue-specific promoters of the ACE2 gene as well as a new putative short form of ACE2. These include several interferon-stimulated response elements sites for STAT1, IRF8, and IRF9. Our results also confirmed that age and gender play no significant role in the regulation of ACE2 mRNA expression in the lung.

Introduction

A zinc metalloenzyme, angiotensin-converting enzyme (ACE) was discovered 64 years ago and first named as a hypertension-converting enzyme [1]. Classically, ACE is well known for its roles in the regulation of arterial pressure through conversion of angiotensin I to active angiotensin II and cleavage of bradykinin and neurotensin [2]. As a zinc metalloenzyme, ACE belongs to a large cluster of zinc-binding proteins. The first zinc metalloenzyme, carbonic anhydrase was discovered in 1932 by Meldrum and Roughton [3] and thereafter thousands of such metalloenzymes have been reported in different species of all phyla [4, 5].

Angiotensin-converting enzyme 2 (ACE2) was first discovered in 2000 when a novel homologue of ACE was cloned [2, 6, 7]. Although ACE and ACE2 share significant sequence similarity in their catalytic domains, they appear to act on different peptide substrates of angiotensins [8, 9]. Previous studies identified ACE2 as a functional receptor for severe acute respiratory syndrome corona virus 1 (SARS-CoV-1) which led to an outbreak of SARS infection in 2003 [10]. ACE2 is also a crucial receptor for the novel corona virus (SARS-CoV-2), which has caused a large global outbreak of COVID-19 infection with rapidly growing numbers of patients (32,968,853 confirmed cases as of September 28th, 2020, https://www.who.int/emergencies/diseases/novel-coronavirus-2019). A recent report suggested that soluble ACE2 fused to the Fc portion of immunoglobulin can neutralize SARS-CoV-2 in vitro [11]. This result was further confirmed by showing that human recombinant soluble ACE2 reduced SARS-CoV-2 infection on cultured Vero-E6 cells in a dose dependent manner [12]. Therefore, ACE2 also holds promise for treating patients with coronavirus infection.

The structural key for target cell infection by coronavirus is the viral spike (S) protein of SARS-CoV. ACE2 acts as a locking device for the virus, whereby the binding of the surface unit S1 facilitates viral attachment to the surface of target cells [13]. The cellular serine protease (TMPRSS2) promotes SARS-CoV entry via a dual mechanism. It cleaves both the SARS-CoV S protein and the virus receptor, ACE2, promoting both the viral uptake and the viral and cellular membrane fusion events [13-15]. The critical residues contributing to the receptor-spike protein interaction were first determined for SARS-CoV-1 [16] and recently in three independent studies for SARS-CoV-2 [17-19]. It has been proposed by biolayer interferometry studies that the receptor-binding domains of SARS-CoV-1 and SARS-CoV-2 S proteins bind with comparable affinities to human ACE2 [20]. In contrast, a modelling study suggested that binding of SARS-CoV-2 is stronger [21], which was convincingly confirmed by structural and biochemical data [17, 18].

The clinical characteristics of COVID-19 infection have recently been described based on data from 1,099 patients from mainland China [22]. It was found that the clinical characteristics of COVID-19 mimic those of SARS-CoV-1 infection. The most dominant symptoms include fever, cough, fatigue, and sputum production, whereas gastrointestinal symptoms are less common. In laboratory parameters, lymphopenia was detected in 83.2% of patients on admission. According to another recent survey of 278 patients with pneumonia caused by SARS-CoV-2, fever was the most common symptom, followed by cough [23]. Bilateral pneumonia has been detected by computed tomography scans in 67.0% of patients [24]. A recent study from Wuhan, China listed the most common clinical complications determined in critically ill COVID-19 patients [25]. The complications during clinical worsening included acute respiratory distress syndrome and respiratory failure, sepsis, acute cardiac injury, and heart failure.

Data on the localization of virus receptors can provide insight into mechanisms of virus entry, tissue tropism, and pathogenesis of the disease. Therefore, it is of particular interest to correlate COVID-19 symptoms with the distribution pattern of ACE2. The first studies performed by northern blotting indicated that ACE2 is located in the human heart, kidney, and testis [2]. Quantitative polymerase chain reaction (qPCR) showed the highest expression levels in the human cardiovascular system, testis, kidney, and intestine [26]. By immunohistochemistry, the expression of the ACE2 protein was identified in the human lung alveolar epithelial cells (type I and II pneumocytes), enterocytes of the small intestine, the brush border of the renal proximal tubules, the endothelial cells of arteries and veins, and arterial smooth muscle cells in several organs [27]. It was proposed that this distribution pattern of ACE2 could explain the tissue tropism of SARS-CoV-1 for the lung, small intestine, and kidney [28]. On the other hand, the symptoms of COVID-19, in contrast to SARS-CoV-1 infection, are not associated to the same extent with the gastrointestinal tract in spite of the high expression of ACE2 in the intestinal enterocytes [29]. In COVID-19, diarrhea has been reported in just 3.8% of patients, in contrast to 40–70% in SARS-CoV-1 infection [22, 30]. A recent report indicated diarrhea in 18.1% of 254 COVID-19 patients [31].

There are conflicting reports on the expression of ACE2 in the upper respiratory tract [30]. Hamming and coworkers found that only the basal layer of nonkeratinized airway squamous epithelium shows positive signal [27], whereas Sims and colleagues demonstrated ACE2 expression on the luminal surface of ciliated cells in freshly excised human nasal and tracheobronchial tissue [32]. Ren and coworkers showed weak ACE2-positive signal in the epithelial cells of trachea and main bronchus [33]. Although lymphopenia is a typical feature of SARS [22, 30], ACE2 is not highly expressed on T or B cells or macrophages in the spleen or lymphoid organs [27].

It is known that both SARS-CoV and SARS-CoV-2 infections lead to worse outcome in the elderly [30, 34]. Recent studies have also indicated higher case fatality rates in males than females [35]. Therefore, one aim of the present study was to investigate whether age or gender could contribute to the regulation of ACE2 expression. We also decided to explore the transcriptional regulation of ACE2 gene expression using a novel computational tool recently developed by the first author of this article. Notably, data on ACE2 distribution is still conflicting, and thus we aimed to get a more comprehensive view of the cell types expressing the receptor of SARS-CoV-2. Finally, we studied the coexpression of ACE2 with other genes and explored its putative functions using a gene ontology enrichment analysis.

Methods

ACE2 mRNA expression

From the FANTOM5 project [36], cap analysis of gene expression (CAGE) sequencing of cDNA has been performed in 1,839 human samples from 875 different primary cells, tissues, and cell lines (description of all public datasets used presented as S1 Table). Expression of transcription start sites (TSSs) was extracted and combined for all genes in all samples as tags per million (TPM). From this compiled set, ACE2 gene expression was extracted and presented as barplot using the Matplotlib [37] and Seaborn [38] Python libraries. Similarly, human gene expression data (as TPM) was extracted from the GTEx database, which is an ongoing large-scale project to identify human variation, regulation, and gene expression [39], along with metadata on the samples. ACE2 gene expression values were separated by tissue and compared among 10-year interval age groups to determine if the values showed any differences throughout the lifecycle. Boxplots for tissues of relevance were generated using Matplotlib and Seaborn libraries.

Coexpression and gene ontology enrichment analysis

In each of the tissues present in the GTEx dataset, expression values for ACE2 were compared with expression of all other genes by Spearman correlation analysis using the SciPy [40] Python library to identify those genes with concordant expression patterns. Bonferroni correction was used to derive an adjusted p-value threshold of 9.158E-07. For each tissue, those genes which both satisfied the Bonferroni-adjusted p-value threshold and had a correlation of expression of 0.50 or greater were analyzed using the Gprofiler gene ontology (GO) enrichment analysis [41] Python library to identify possible enriched terms in biological process (BP), molecular function (MF), cellular component (CC), human phenotype (HP), KEGG pathway, and WikiPathways (WP) ontologies.

ACE2 protein expression

Immunohistochemical localization of human ACE2 was evaluated from immunostained specimens provided by Protein Expression Atlas (https://www.proteinatlas.org/) [42]. The dataset included three specimens of duodenum, three specimens of kidney, three specimens of testis, three specimens of lung, and two specimens of nasopharynx. The images of the Fig 2 represent duodenum from 77-years-old female, kidney from 36-years-old male, testis from 38-years-old male, lung from 61-years-old female, and nasopharyngeal mucosa from 78-years-old female. According to Protein Expression Atlas the immunostainings were performed with the rabbit anti-human polyclonal antibody (HPA000288; Sigma Aldrich, St. Louis, MO) raised against 111 N-terminal amino acids of ACE2 and diluted 1:250 for the staining.

Fig 2

Immunohistochemical localization of ACE2 protein in selected human tissues.

In the duodenum (A), the protein is most strongly localized to the apical plasma membrane of absorptive enterocytes (arrows). The goblet cells (arrowheads) show weaker apical staining. Intracellular staining is confined to the absorptive enterocytes. In the kidney (B), ACE2 shows strong apical staining in the epithelial cells of the proximal convoluted tubules (arrows) and Bowman´s capsule epithelium (arrowheads). The distal convoluted tubules are negative (asterisk). The testis specimen (C) shows strong immunostaining in the seminiferous tubules (arrows) and interstitial cells (arrowheads). The lung sample (D) is negative. In the nasopharyngeal mucosa (E), ACE2 signal is very weak and only occasional epithelial cells show weak signals (arrows). Immunostained specimens were taken from the Protein Expression Atlas (https://www.proteinatlas.org/).

Promoter analysis

Analysis of ACE2 promoter regions was performed using the TFBSfootprinter tool (https://github.com/thirtysix/TFBS_footprinting) which uses transcription-relevant data from several major databases to enhance prediction of putative TFBSs, including: all cell types aggregated and merged human ATAC-Seq data from ENCODE [43], transcription start sites and expression data from FANTOM5 [44], expression quantitative trail loci from GTEx [39], TFBS metacluster data from GTRD [45], TFBS binding profile data from JASPAR [46], and sequence and conservation data from Ensembl [47]. Detailed description of this novel tool is under preparation [48]. Previous studies identified two distinct tissue-specific transcription start sites (TSS) for intestine and lung expression [49], which correspond to primary protein-coding Ensembl transcripts ENST00000252519 and ENST00000427411, respectively. These two transcripts were targeted for transcription factor binding site (TFBS) analysis; first with a scan for all 575 Jaspar TFs and input parameters of 1,000 base pairs (bp) upstream and 200 bp downstream (relative to the TSS); secondly with a limited set of 15 interferon-stimulated TF genes and a broader area of 1,500 bp upstream and 500 bp downstream. Likewise, an analysis of the promoter region of a putative new short form of ACE2 was performed.

Single-cell RNA-Seq

Single-cell expression datasets were identified for relevant tissues/cells of lung (human) [50], trachea (mouse) [51], and small intestine (mouse) [52]. Using a modified workflow described previously in [53], for each dataset the samples were filtered by Gaussian fit of read count, expressed gene count, and number of cells in which a gene is expressed. Counts were normalized by cell, log transformed, principle component analysis performed with 15 components, and k-nearest neighbors computed using SCANPY [54], and then the full dataset normalized with R package ‘scran’ [55]. Batch correction by individual and sample region was performed with SCANPY using the ComBat function. The top 1,000 genes with highly differential expression were identified for cluster analysis which was performed with Uniform Manifold Approximation and Projection (UMAP) and force directed graph models. The top 100 marker genes were identified as those with higher expression unique to each cluster by Welch t-test in SCANPY. Expression of the ACE2 gene was mapped onto cluster figures to determine overlap with previously identified cell types or cell type marker genes identified in the literature. Cell type was mapped by expression of known marker genes of cell types expressed in the lung and small intestine, as defined by de novo prediction in the original articles.

Statistics

Comparisons of ACE2 expression values in different tissues and between groups delineated by age or sex, were carried out by one-way ANOVA using the stats package in the SciPy [40] Python library. Only groups with 20 or more observations and a 2-sided chi squared probability of normality of < = 0.1 (due to the robustness of ANOVA to non-normal distributions) were used for comparison. Correlation of gene expression values was calculated by two-sided Spearman rank-order analysis, where a Bonferroni-corrected p-value threshold was computed using α = 0.05/number of comparisons. Gene ontology enrichment analyses performed using the GProfiler tool utilize a custom algorithm for multiple testing of dependent results, which corresponds to an experiment-wide threshold of α = 0.05. TFBSfootprinter analysis of the ACE2 promoter limits results for individual TFBSs whose score satisfies a genome-wide threshold of α = 0.01.

Results

ACE2 is weakly expressed in the lung

The first aim of our study was to investigate different human tissues using publicly available datasets for the distribution of ACE2 mRNA and protein. In the FANTOM5 dataset, the highest values for ACE2 mRNA, ranked according to signal intensity, were seen for the small intestine, dura mater, colon, testis, thalamus, and rectum (Fig 1).

Fig 1

Expression of ACE2 mRNA in selected human tissues.

Expression values as TPM have been extracted from the FANTOM5 dataset.

Fig 2 shows the expression of ACE2 protein in selected human tissues. Representative example images of the ACE2 immunostaining were prepared from tissue specimens of the Human Protein Atlas database (https://www.proteinatlas.org/). The results indicate a strong signal for ACE2 protein in the brush border of small intestinal enterocytes. In the kidney, strong immunostaining reactions were present in the epithelial cells of proximal convoluted tubules and Bowman´s capsule. The seminiferous tubules and interstitial cells of testis also demonstrated strong immunostaining. No immunoreactions for ACE2 were observed in the lung specimens. Very weak signal, associated with apical membranes, was detected in sporadic ciliary cells of a nasopharyngeal mucosa sample. Although the evaluation of immunostaining reaction is generally considered semiquantitative at most, the results seem to correlate fairly well with the corresponding mRNA expression levels.

Single cell RNA-Seq analysis indicates cell-specific expression for ACE2 mRNA

The respiratory tract is the main target region that is affected by COVID-19 infection. Bulk RNA-Seq data from lung specimens showed low expression levels for ACE2 (Fig 1). Therefore, we performed an analysis of single cell RNA-Seq using both human lung and mouse trachea datasets, representing the breadth of the lower respiratory tract. Figs 3 and 4 show the expression of ACE2 mRNA in identified cell types of lung and trachea, respectively. In lung, ACE2 expressing cells are generally uncommon with no cell type having a ratio of ACE2-expressing cells greater than 2.6%. The cell types with the greatest proportion of ACE2 expression are those of arterial vascular endothelial cells (2.55%), goblet cells (2.02%), and venous vascular endothelial cells (1.33%). In trachea, the highest ratio of ACE2-expressing cells included the club cells (16.62%), goblet cells (13.84%), and ciliary epithelial cells (6.63%).

Fig 3

Single cell RNA-Seq analysis of different cell types from the lung (human), derived from data from GEO dataset GSE136831 [50].

ACE2 mRNA expression as normalized, batch-corrected counts is shown for comparison in upper panel. The force directed layout plot was computed and visualized in ScanPy [54]. For each cell type the ratio of cells expressing ACE2 is presented in addition to a stacked barplot of the relative cell type frequencies in the whole dataset. Alveolar type I (ATI), alveolar type II (ATII), pulmonary neuroendocrine cells (PNEC), smooth muscle cells (SMC), vascular endothelia (VE).

Fig 4

Single cell RNA-Seq analysis of different cell types from the respiratory tract (mouse tracheal epithelium), derived from data from GEO dataset GSE103354 [51].

ACE2 mRNA expression as normalized, batch-corrected counts is shown for comparison in upper panel. The force directed layout plot was computed and visualized in ScanPy [54]. For each cell type the ratio of cells expressing ACE2 is presented in addition to a stacked barplot of the relative cell type frequencies in the whole dataset.

Since both the airways and intestine contain goblet cells, SARS-CoV-1 affects gastrointestinal tract, and bulk RNA-Seq data shows high expression in small intestine and colon, we decided to analyze another single cell RNA-Seq dataset covering mouse intestinal epithelial cells. Fig 5 indicates the highest levels of ACE2 mRNA signal in the absorptive enterocytes (44.09%), whereas the intestinal goblet cells (1.35%) remain mostly negative.

Fig 5

Single cell RNA-Seq analysis of mouse intestinal epithelial cells.

Data is from GEO dataset GSE92332 [52]. ACE2 mRNA expression as normalized, batch-corrected counts is shown for comparison in upper panel. The force directed layout plot was computed and visualized in ScanPy [54]. For each cell type the ratio of cells expressing ACE2 is presented in addition to a stacked barplot of the relative cell type frequencies in the whole dataset.

ACE2 mRNA expression levels are unrelated to age and gender in the lung

Since both age and gender may contribute to onset and severity of COVID-19 symptoms we aimed to investigate the effect of these variables on the expression levels of ACE2 mRNA. Fig 6 indicates that some tissues showed a slight trend to lower expression in older age categories. Among all tested tissues, statistically significant differences between the age categories were seen in the tibial nerve (p = 8.58 x 10−6), minor salivary gland (p = 0.002), aorta (p = 0.003), whole blood (p = 0.005), transverse colon (p = 0.010), hypothalamus (p = 0.039), and sun exposed skin (p = 0.046). Importantly, the lung specimens showed no significant difference of ACE2 mRNA expression between different age categories (p = 0.681). Complete data on ACE2 mRNA expression levels in different age categories are shown in S2 Table. To make a binary comparison of expression by age, samples were divided into groups of <50 and ≥50 years of age. In comparison of these younger and older age groups, significant differences in expression were found in tibial nerve (p = 2.47 x 10−7), whole blood (p = 3.21 x 10−4), minor salivary gland (p = 4.89 x 10−4), sun exposed skin (p = 0.003), transverse colon (p = 0.022), testis (p = 0.025), esophageal muscle layer (p = 0.040), and subcutaneous adipose tissue (p = 0.045). Additionally, with the same age groups, comparisons were made for both males and females (S3 Table). In males, ACE2 expression was lower in the ≥50 age group for tibial nerve (p = 1.33 x 10−7), subcutaneous adipose tissue (p = 1.23 x 10−4), minor salivary gland (p = 4.03 x 10−4), transverse colon (p = 4.67 x 10−3), sigmoid colon (p = 0.017), testis (p = 0.025), visceral adipose tissue of omentum (p = 0.026), sun exposed skin (p = 0.027), whole blood (p = 0.032), and bladder (p = 0.042); while increased in coronary artery (p = 0.015). In females, ACE2 expression was lower in the ≥50 age group in whole blood (p = 0.005) and sun exposed skin (p = 0.049); and higher in esophagus (p = 0.007) and terminal ileum (p = 0.022).

Fig 6

Effect of age on ACE2 mRNA expression levels.

Data is extracted from the GTEx dataset as TPM. In these organs, ANOVA revealed significant differences between age categories in tibial nerve (p = 8.58 x 10−6), minor salivary gland (p = 0.002), and whole blood (p = 0.005). In other tissues, the differences did not reach statistical significance. The highest TPM values are seen in the small intestine, testis, and kidney.

ACE2 mRNA levels largely overlapped between male and female sexes as shown in Fig 7. In the lung, no statistically significant difference was observed in the expression levels between the male and female subjects (p = 0.908). Statistically significant differences were observed in the adipose tissue (p = 0.0001), whole blood (p = 0.0002), amygdala (p = 0.0006), transverse colon (p = 0.0008), muscle layer of esophagus (p = 0.002), left ventricle of heart (p = 0.005), Epstein-Barr virus-transformed lymphocytes (p = 0.015), and esophagus-gastroesophageal junction (p = 0.024). Notably, there was no clear sex-specific trend pointing to one direction in all these cases. ACE2 mRNA expression levels in all studied tissues sorted according to subjects´ gender are shown in S4 Table.

Fig 7

Effect of gender on ACE2 mRNA expression levels.

Data is extracted from the GTEx dataset as TPM. The expression levels in males and females overlap in all tissue categories. Statistically significant differences studied by ANOVA analysis were determined in esophagus-gastroesophageal junction (p = 0.024) and whole blood (p = 0.0002). The ACE2 mRNA expression levels in testis specimens are shown here for comparison. 1 = male, 2 = female.

Proximal promoter contains putative TFBSs for ileum, colon, and kidney expression

TFBS analysis of the ACE2 intestinal transcript promoter (ENST00000252519) revealed several candidate binding sites which occur in a cluster extending from 400 bp upstream of the transcription start site; CDX2, HNF1A, FOXA1, SOX4, TP63, HNF4A, DUX4, FOXA2, NR2F6, and SOX11 (Fig 8A). These predicted sites overlap an evolutionarily conserved region in mammals and are proximal to several ATAC-Seq peaks. In several tissues these TFs are found to be highly positively correlated (>0.7) with expression of ACE2, as determined using RNA-Seq gene expression values from the GTEX dataset: CDX2 (colon, terminal ileum), HNF1A (colon, kidney, terminal ileum), FOXA1 (cervix, colon, terminal ileum), HNF4A (colon, terminal ileum), FOXA2 (colon, kidney), NR2F6 (colon, kidney, terminal ileum), and SOX11 (kidney). In addition, two of the TFs are highly negatively correlated with ACE2 expression DUX4 (kidney) and FOXA1 (kidney). Full prediction results are included in S5 Table and TF correlations by tissue are present in S6 Table. An expanded analysis for binding sites of interferon-stimulation mediating TF genes in the region 1,500 bp upstream to 500 bp downstream of the ACE2 intestinal transcript’s TSS revealed putative binding sites for STAT1 (3x), STAT3 (4x), STAT5A:STAT5B dimer (3x), STAT4, STAT1:STAT2 dimer (2x), IRF2, IRF3, IRF4 (2x), IRF5 (2x), IRF8 (3x), and IRF9 (S1 Fig). Each of these predictions satisfied a PWM p-value threshold of <0.0001, while only the STAT1, IRF9, and IRF8 sites also satisfied a combined affinity score p-value of <0.05.

Fig 8

Prediction of transcription factor binding sites in the human ACE2 gene promoter regions of Ensembl transcripts ENST00000252519 and ENST00000427411 using TFBSfootprinter.

A) Promoter region of the intestine specific ACE2 transcript. The results show putative binding sites for several transcription factors which have a strong correlation of expression with ACE2 in colon, kidney, and ileum. The predicted binding sites overlap regions of conservation in mammal species (Ensembl GERP), and cluster within 400 base pairs (bp) of the transcription start site. B) Promoter region of the lung specific ACE2 transcript. The predicted binding sites cluster within 200 bp of the TSS and overlap regions of conservation in mammal species, ATAC-Seq peaks (ENCODE), and TFBS metaclusters (GTRD).

Analysis of the ACE2 lung transcript promoter (ENST00000427411) produced putative TFBS predictions for ESRRA, HNF4A, CDX2, CEBPA, ESRRB, MEF2B, TCF7, TCF7L2, JUN, and LEF1 (Fig 8B). Full prediction results are included in S5 Table. The predicted TFBSs clustered within 200 base pairs of the TSS, and overlap with evolutionarily conserved regions, TFBS metaclusters, and ATAC-Seq peaks. The TFs corresponding to predicted TFBSs, which are positively correlated (>0.7) with ACE2 expression, are ESRRA (terminal ileum, colon), HNF4A (terminal ileum, colon), CDX2 (colon, terminal ileum), CEBPA (colon, terminal ileum), ESRRB (cervix), TCF7L2 (testis). Those TFBSs with TFs which strongly (<-0.7) negatively correlate with ACE2 are ESRRA (kidney) and TCFL72 (kidney).

The lung-specific transcript TSS aligns with the p3@ACE2 FANTOM5 dataset CAGE peak, which indicates that the expression of this transcript is much lower than the intestinal transcript, which corresponds with p1@ACE2 and p2@ACE2 FANTOM5 CAGE peaks. Common between the two tissue-specific transcripts, are predictions for CDX2 and HNF-family transcription factors. An expanded analysis for binding sites of interferon-stimulation mediating TF genes in the region 1,500 bp upstream to 500 bp downstream of the lung transcript’s TSS revealed putative binding sites for STAT1:STAT2 dimer, STAT1 (2x), STAT3, STAT4, STAT6 (2x), and IRF8 (S1 Fig). Each of these predictions satisfied a PWM p-value threshold of <0.0001, while only the IRF8 site also satisfied a combined affinity score p-value of <0.05.

TFBS analysis of the recently identified putative short form ACE2 transcript [56-58], with a TSS between exons 9 and 10 of the canonical gene, produced predictions for IRF9, IRF8, JUND, FOSL1, GATA1, JUNB, IRF4, JUN, and FOS, among others (S5 Table). Within the first -56 to -31 bp upstream of the short-form ACE2 TSS, are overlapping binding sites for several IRF TFs and a STAT1:STAT2 dimer, while further upstream are binding sites for several STAT TFs at -662 to -647 bp and -911 to –897 (Fig 9). Each of these predictions satisfied a PWM p-value threshold of <0.001, while only the IRF9, IRF8, and IRF4 sites also satisfied a combined affinity score p-value of <0.05.

Fig 9

Prediction of interferon-stimulation mediating transcription factor binding sites in the promoter region of the putative short-form of human ACE2 gene using TFBSfootprinter.

The long terminal repeat 16A1 (LTR16A1) is cataloged in the FANTOM dataset [36] and has been identified as potentially relevant for interferon-mediated transcription of this new ACE2 transcript [56]; it is displayed in the CAGE peaks track along with an arrow indicating the TSS. The region analyzed represents –1,000 bp to +200 bp relative to the putative TSS, while nucleotide positions at bottom are given relative to the ACE2 full length transcript.

ACE2 mRNA expression correlates with metalloproteases and transporter genes

Coexpression analysis identified numerous genes in ileum, testis, colon, and kidney which are highly correlated (>0.8) with ACE2 (Tables 1 and S7). In particular, in the ileum there are a number of genes with correlation values greater than 0.95. In contrast, analysis of the lung shows a maximum correlation of expression of 0.6275. The genes with which ACE2 mRNA expression shows the highest levels of coexpression code for metalloprotease and transporter proteins. Selected tissue-specific ACE2-correlated genes, determined with bulk RNA-Seq data, are presented with their expression levels within the scRNA-Seq trachea and intestinal datasets in Fig 10.

Table 1

Genes associated with ACE2 mRNA expression in selected human tissues.

Derived from GTEx bulk RNA-Seq data [39].

Tissue	Correlated_gene	Correlation	p-value	HGNC	UniProt	Description	Panther protein class
terminal ileum	ENPEP	0.9653	8.29E-110	3355	Q07075	Glutamyl aminopeptidase	metalloprotease (PC00153)
terminal ileum	MEP1B	0.9653	8.35E-110	7020	Q16820	Meprin A subunit beta	metalloprotease (PC00153)
terminal ileum	SLC15A1	0.9621	2.19E-106	10920	P46059	Solute carrier family 15 member 1	transporter (PC00227)
terminal ileum	MEP1A	0.9619	4.4E-106	7015	Q16819	Meprin A subunit alpha	metalloprotease (PC00153)
terminal ileum	SLC3A1	0.9608	5.63E-105	11025	Q07837	Neutral and basic amino acid transport protein rBAT	amylase (PC00048)
terminal ileum	APOB	0.9587	6.45E-103	603	P04114	Apolipoprotein B-100
terminal ileum	SLC6A19	0.9581	2.33E-102	27960	Q695T7	Sodium-dependent neutral amino acid transporter B(0)AT1	primary active transporter (PC00068)
terminal ileum	ANPEP	0.9562	1.16E-100	500	P15144	Aminopeptidase N	metalloprotease (PC00153)
terminal ileum	MTTP	0.9559	2.45E-100	7467	P55157	Microsomal triglyceride transfer protein large subunit	transporter (PC00227)
terminal ileum	TUBAL3	0.9554	6.24E-100	23534	A6NHL2	Tubulin alpha chain-like 3	tubulin (PC00228)
testis	PLP1	0.9092	1.07E-138	9086	P60201	Myelin proteolipid protein	myelin protein (PC00161)
colon-transverse	MEP1A	0.9056	1.52E-152	7015	Q16819	Meprin A subunit alpha	metalloprotease (PC00153)
kidney	TINAG	0.9012	7.07E-32	14599	Q9UJW2	Tubulointerstitial nephritis antigen	cysteine protease (PC00081)
colon-transverse	GDA	0.8964	8.19E-145	4212	Q9Y2T3	Guanine deaminase	deaminase (PC00088)
colon-transverse	MGAM2	0.8963	9E-145	28101	Q2M2H8	Probable maltase-glucoamylase 2	glucosidase (PC00108)
colon-transverse	TMEM236	0.894	6.66E-143	23473	Q5W0B7	Transmembrane protein 236
colon-transverse	GDPD2	0.8935	1.58E-142	25974	Q9HCC8	Glycerophosphoinositol inositolphosphodiesterase GDPD2
colon-transverse	PLS1	0.8932	2.81E-142	9090	Q14651	Plastin-1	non-motor actin binding protein (PC00165)
colon-transverse	HHLA2	0.893	3.59E-142	4905	Q9UM44	HERV-H LTR-associating protein 2	immunoglobulin receptor superfamily (PC00124)
colon-transverse	SLC3A1	0.8928	5.27E-142	11025	Q07837	Neutral and basic amino acid transport protein rBAT	amylase (PC00048)
colon-transverse	COL17A1	0.8914	6.12E-141	2194	Q9UMD9	Collagen alpha-1(XVII) chain	extracellular matrix structural protein (PC00103)
testis	NDFIP1	0.8911	2.91E-125	17592	Q9BT67	NEDD4 family-interacting protein 1
testis	SPARC	0.8908	4.67E-125	11219	P09486	SPARC	extracellular matrix glycoprotein (PC00100)
colon-transverse	TINAG	0.8905	2.94E-140	14599	Q9UJW2	Tubulointerstitial nephritis antigen	cysteine protease (PC00081)
testis	SCP2	0.8886	1.34E-123	10606	P22307	Non-specific lipid-transfer protein	transfer/carrier protein (PC00219)
testis	SMARCA1	0.8852	2.44E-121	11097	P28370	Probable global transcription activator SNF2L1	DNA helicase (PC00011)
testis	ATP2B1	0.8829	6.45E-120	814	P20020	Plasma membrane calcium-transporting ATPase 1	primary active transporter (PC00068)
testis	VSIG1	0.8815	4.6E-119	28675	Q86XK7	V-set and immunoglobulin domain-containing protein 1
testis	HTATSF1	0.8809	1.09E-118	5276	O43719	HIV Tat-specific factor 1	RNA splicing factor (PC00148)
testis	ACO1	0.8808	1.26E-118	117	P21399	Cytoplasmic aconitate hydratase	RNA binding protein (PC00031)
testis	ENPP5	0.8802	3.01E-118	13717	Q9UJA9	Ectonucleotide pyrophosphatase/phosphodiesterase family member 5	nucleotide phosphatase (PC00173)
kidney	METTL7B	0.8705	2.78E-27	28276	Q6UX53	Methyltransferase-like protein 7B	methyltransferase (PC00155)
kidney	ANPEP	0.8683	5.3E-27	500	P15144	Aminopeptidase N	metalloprotease (PC00153)
kidney	LRP2	0.8671	7.59E-27	6694	P98164	Low-density lipoprotein receptor-related protein 2
kidney	SLC13A1	0.8667	8.5E-27	10916	Q9BZW2	Solute carrier family 13 member 1	secondary carrier transporter (PC00258)
kidney	UGT3A1	0.8608	4.52E-26	26625	Q6NUS8	UDP-glucuronosyltransferase 3A1
kidney	SLC27A2	0.8535	3.26E-25	10996	O14975	Very long-chain acyl-CoA synthetase	secondary carrier transporter (PC00258)
kidney	TMEM27	0.8526	4.1E-25	29437	Q9HBJ8	Collectrin
kidney	CLRN3	0.8443	3.36E-24	20795	Q8NCR9	Clarin-3
kidney	ACP5	0.8367	2.05E-23	124	P13686	Tartrate-resistant acid phosphatase type 5
lung	PARM1	0.6275	1.32E-64	24536	Q6UWI2	Prostate androgen-regulated mucin-like protein 1
lung	CNR1	0.6055	4.21E-59	2159	P21554	Cannabinoid receptor 1	G-protein coupled receptor (PC00021)
lung	NUDT16	0.5918	6.52E-56	26442	Q96DE0	U8 snoRNA-decapping enzyme
lung	DUOX1	0.5817	1.23E-53	3062	Q9NRD9	Dual oxidase 1	oxidase (PC00175)
lung	DUOXA1	0.5739	6.2E-52	26507	Q1HG43	Dual oxidase maturation factor 1
lung	FMN1	0.571	2.53E-51	3768	Q68DA7	Formin-1
lung	TMEM164	0.5692	6.05E-51	26217	Q5U3C3	Transmembrane protein 164
lung	HSD17B4	0.5673	1.54E-50	5213	P51659	Peroxisomal multifunctional enzyme type 2
lung	MGST1	0.5631	1.16E-49	7061	P10620	Microsomal glutathione S-transferase 1
lung	SHMT1	0.5594	6.77E-49	10850	P34896	Serine hydroxymethyltransferase, cytosolic	methyltransferase (PC00155)

Fig 10

Expression of genes most highly correlated with ACE2 in single cell datasets of trachea and intestinal epithelia.

Trachea expression data is taken from GSE103354 [51] and intestinal epithelia data is derived from GSE92332 [52]. Visualized in ScanPy [54].

ACE2 is associated with vascular growth

GO enrichment analysis of ACE2 mRNA expression in all tissues produced 22 terms which were enriched in BP, CC, HP, KEGG, and WP ontologies (Table 2). A total of 12 of these terms were related to blood vessel growth, including the three most strongly enriched terms, ‘angiogenesis [GO:0001525]’, ‘blood vessel morphogenesis [GO:0048514]’, and ‘vasculature development [GO:0072358]’. Full GO enrichment results for relevant tissues (lung, small intestine, kidney, colon, and testis) are included as S8 Table.

Table 2

Gene ontology annotation results for the processes associated with genes strongly coexpressed (≥0.5) with ACE2 across all tissues in GTEx dataset.

Calculated with GProfiler Python library [41].

Source	Native	Process	p-value
GO:BP	GO:0001525	angiogenesis	1.797E-08
GO:BP	GO:0048514	blood vessel morphogenesis	4.611E-08
GO:BP	GO:0001944	vasculature development	1.893E-07
GO:BP	GO:0072358	cardiovascular system development	2.365E-07
GO:BP	GO:0001568	blood vessel development	4.746E-07
GO:BP	GO:0035239	tube morphogenesis	5.907E-07
GO:BP	GO:0072359	circulatory system development	6.641E-07
GO:BP	GO:0048646	anatomical structure formation involved in morphogenesis	1.885E-05
GO:BP	GO:0035295	tube development	2.900E-05
GO:BP	GO:1901342	regulation of vasculature development	0.025
GO:BP	GO:0008217	regulation of blood pressure	0.035
GO:BP	GO:0045765	regulation of angiogenesis	0.009
GO:CC	GO:0071944	cell periphery	0.001
GO:CC	GO:0009986	cell surface	0.002
GO:CC	GO:0005886	plasma membrane	0.013
GO:CC	GO:0046930	pore complex	0.003
HP	HP:0005381	recurrent meningococcal disease	0.004
HP	HP:0005430	recurrent Neisserial infections	0.007
HP	HP:0100601	eclampsia	0.021
KEGG	KEGG:04610	complement and coagulation cascades	0.002
KEGG	KEGG:04923	regulation of lipolysis in adipocytes	0.037
WP	WP:WP558	complement and coagulation cascades	0.011

Discussion

The predominant pathological features of COVID-19 infection largely mimic those previously reported for SARS-CoV-1 infection. They include dry cough, persistent fever, progressive dyspnea, and in some cases acute exacerbation of lung function with bilateral pneumonia [32]. Major lung lesions include several pathological signs, such as diffuse alveolar damage, inflammatory exudation in the alveoli and interstitial tissue, hyperplasia of fibrous tissue, and eventually lung fibrosis [59-61]. It has been shown by fluorescence in situ hybridization technique that SARS-CoV-1 RNA locates to the alveolar pneumocytes and alveolar space [62, 63]. Mossel and colleagues demonstrated that SARS-CoV-1 replicates in type 2 (AT2) pneumocytes, but not in type 1 (AT1) cells [64]. Considering all of these facts, it is not surprising that most histopathological analyses have been focused on distal parts of the respiratory airways, while the regions other than the alveolus have been less systematically studied.

To understand better the pathogenesis of COVID-19 we need to know where ACE2, the receptor for SARS-CoV, is located within the human respiratory tract and elsewhere. Overall, different studies including ours have convincingly shown that several organs, such as the small intestine, colon, kidney, and testis, express higher levels of ACE2 than the lung and other parts of the respiratory tract. Our analysis of ACE2 expression in the human lung show low levels of expression in all cell types, with arterial vascular endothelial cells achieving the highest overall ratio of just ~2.5%. The present results based on mouse tracheal dataset suggested that ACE2 mRNA is predominantly expressed in the club cells, goblet cells, and ciliated epithelial cells, and at significantly higher frequency than found in the lung. The mouse dataset used in our study contained no secretory3 cells, which Lukassen and colleagues recently reported to express the highest levels of ACE2 mRNA along the human respiratory tract [65]. Another study reported positive expression in the type AT2 pneumocytes [66], which is in line with the results of Lukassen et al. [65], but only a few cells appeared positive. A third study based on single cell expression data demonstrated the strongest positive signal in the lung AT2 cells, while other cells including AT1 cells, club cells, ciliated cells, and macrophages showed weaker expression [67]. A fourth single cell expression analysis using Gene Expression Omnibus (GEO) database recently demonstrated ACE2-positive signal in 1% of AT2 cells and in 2% of respiratory tract epithelial cells [68]. This correlates with our own findings for lung. For comparison about 30% of ileal epithelial cells were ACE2-positive, and 44% of enterocytes in small intestine of mouse. Immunohistochemical analysis of mouse tissues has shown positive signal in the club cells, AT2 cells, endothelial cells, and smooth muscle cells [69]. In spite of the obvious discrepancies between different datasets, that highlights the need for large numbers of thoroughly characterized cells for single cell RNA-Seq analyses, we can now make some conclusions of the expression of ACE2 mRNA in the respiratory tract. First, ACE2 is positively though weakly expressed in the AT2 cells of the lung and less so in AT1 cells. Second, ACE2 also shows a weak positive signal, but at significantly higher proportions of cells, in several other cell types of the trachea, including goblet cells, club cells, and ciliated cells. Third, based on the findings of Lukassen et al. [65] secretory3 cells, a transient cell type of the bronchial tree, may express the highest levels of ACE2. These ACE2-positive cell types may represent the main host cells for SARS-CoV-2 along the whole respiratory tract. However, the median percentage of ACE-expressing secretory3 cells in the study was less than 6%, significantly less than that observed in club (16.62%), goblet (13.84%), and ciliated (6.63%) cells of trachea we have identified in the GSE103354 dataset.

Goblet cells, ciliated epithelial cells, and club cells are considered important cell types for the protection of airway mucosa. Lukassen and coworkers [65] described secretory3 cells as intermediate cells between goblet, ciliated, and club cells. If SARS-coronaviruses predominantly attack these cells, locating along the airway segments including the trachea, bronchi, and bronchioles until the last segment that is the respiratory bronchioles, it would be obvious that physiological protective mechanisms are severely affected. Defective mucosal protection and inefficient removal of pathogens due to viral infection may contribute to onset of severe bilateral pneumonia that is common for SARS-diseases [70]. This pathogenic mechanism is supported by previous findings, showing that early disease is manifested as a bronchiolar disease with respiratory epithelial cell necrosis, loss of cilia, squamous cell metaplasia, and intrabronchiolar fibrin deposits [32]. In fact, it has been suggested that early diffuse damage as a result of SARS-CoV-1 infection may actually initiate at the level of the respiratory bronchioles [71, 72].

Our findings confirm that the respiratory tract tissues have quite limited expression levels of ACE2 compared to several other tissues that show much more prominent signal. Because ACE2 is highly expressed in the intestine [29], as also confirmed by our bioinformatics study, it would be obvious to predict that both SARS-CoV-1 and -2 infections cause significant gastrointestinal pathology and symptoms including diarrhea. Interestingly, the patients with COVID-19 have reported less gastrointestinal symptoms than the SARS-CoV-1-infected patients [22, 30]. The pathophysiological basis for this phenomenon is not understood at this point, and thus further investigations on this topic are warranted.

When we initiated the present study, we hypothesized that understanding better the transcriptional regulation of the ACE2 gene might help to explain the peculiar distribution pattern of ACE2 in tissues. Since upregulation of ACE2 would reflect an increased number of SARS-coronavirus receptors on cell surfaces, it could possibly help us to understand the mechanisms why certain patients (males more than females, old more than young, smokers more than non-smokers) are more susceptible to the most detrimental effects of the COVID-19 infection. In our study, the signals for ACE2 mRNA in the lung specimens did not vary much in different age groups nor did they show significant differences between males and females, which is in line with previous findings [65]. Therefore, different expression levels of lung ACE2 may not explain the variable outcome of the disease concerning age groups and genders. Importantly, our studies on this aspect were performed using whole tissue RNA-Seq values, and at least one other analysis using single-cell RNA-Seq data has identified changes in ACE2 expression associated age, sex, and smoking status for various cell types [73]. Specifically, they have found ACE2 expression to increase with age in basal and multiciliated cells, and higher expression for males in airway secretory cells and alveolar AT2 cells. Additionally, a study of ACE2 expression in nasal epithelium (not included in GTEx dataset) showed lowest levels in young children (<10) with increasing values in later age groups [74]. It has been recently discussed that different innate and adaptive immune responses related to both age and gender may contribute to variable outcome of severe viral diseases [35]. It is clearly one major research area to be followed regarding COVID-19 infection.

To investigate the transcriptional regulation of the ACE2 gene we made predictions for the binding sites of transcription factors within the proximal promoter region of the intestine-specific and lung-specific human ACE2 transcript promoters. Our findings introduced several putative binding sites in the ACE2 promoter for known transcription factors, which showed high levels of coexpression with ACE2 in several tissues including the ileum, colon, and kidney. The identified transcription factors could represent potential candidate target molecules which regulate ACE2 expression. Two of our predictions, for HNF1A and HNF1B, have been previously identified experimentally to drive ACE2 expression in pancreatic islet cells and insulinoma cells, respectively [49]. Later work by the same group has shown that our prediction of FOXA binding sites in the ACE2 promoter are also likely correct [75]. It is of interest that ACE2 might be regulated by oxygen status. Zhang and coworkers previously demonstrated that ACE2 mRNA and protein levels increased during the early stages of hypoxia and decreased to near-baseline levels at later stages after hypoxia inducible factor (HIF)-1α accumulation [76]. Based on these findings ACE2 has been listed as a HIF1α-target gene [77], although it does not follow the typical HIF1α regulated expression pattern, nor is there any predicted HIF1α binding site in our analyses. However, HNF1B has been identified as upregulated in hypoxia in kidney, independent of HIF1α [78], and in hypoxic embryonic stem cells HIF1α has been shown to increase expression of transcription factors TCF7/LEF1 (predicted to bind the promoter of the lung-specific ACE2 transcript) through Wnt/β-catenin signaling [79].

Recent work has shown that ACE2 expression is stimulated by interferon alpha (IFN-α) in vitro and computationally identified evidence for STAT1, STAT3, IRF8, and IRF1 TFBSs in the ACE2 promoter [80]. Another recent study has identified correlations of expression between ACE2 and other interferon stimulated genes [81]. Our analysis produced several putative binding sites for interferon-stimulation mediating TF genes, proximal to the TSSs of the intestine-specific (STAT1, IRF8, and IRF9) and lung-specific transcript (IRF8). The findings of these studies, and our own, potentially reveal a scenario where SARS-CoV-2 infection itself may induce expression of ACE2 and thus provide a self-perpetuating route of increased cellular infection. This could explain how such low overall ACE2 expression in normal lungs translates into a fatal disease state. However, a series of recent pre-print articles have identified a previously unreported novel ACE2 transcript with a transcription start site occurring between exons 9 and 10 of the canonical ACE2 gene. Alternatively, this truncated transcript has been named LTR16A1-ACE2 [56], delta-ACE2 [57], or simply 'short ACE2' [58]. In all cases the authors have concluded that this new transcript is more strongly stimulated by interferon, though their results differ on whether transcription of the long form is [58] or is not [56, 57] interferon-related. Importantly, a protein resulting from this transcript would lack the 356 amino acids of the first 9 exons, and thus not contain the known SARS-CoV-2 binding domains, and all three studies have hypothesized that the short form ACE2 would not likely be a point of viral entry. One study was unsuccessful in attempting to produce a viable protein from the short transcript [56], while others [57, 58] identified peptides indicative of the short ACE2 protein in data from mass-spec analysis of cancer samples (ovary, colon, breast) from The Cancer Genome Atlas (TCGA). In addition, Blume and coworkers were able to use Western blotting of an ACE2 antibody targeting the C-terminal domain to identify appropriately sized bands (~50kDa), expected of the short form, in nasal epithelial cells and bronchial epithelial cells [58]. All three studies found that the ratio of expression of the short form to long form is highest in nose and mouth and reduces as you progress down the airways and digestive tracts.

Ng et al. in [56] have identified a long terminal repeat (LTR) (LTR16A1) occurring in intron 9 of ACE2, comprising most of the first exon of short-form ACE2, which they posit may be related to the observed interferon reactivity. Our TFBS analysis of LTR16A1 and the promoter of the short ACE2 transcript revealed putative binding sites for multiple IRF proteins and a STAT1:STAT2 dimer immediately upstream (<50 bp) of the newfound TSS and LTR16A1 (Fig 9), and STAT protein binding sites further upstream. While all three studies promote this new short form of ACE2 as more strongly upregulated by interferon, two [57, 58] also contain results that show that long form ACE2 is itself also upregulated, to a lesser extent, in presence of interferon or infection.

These three manuscripts represent the leading edge of, and first forays into, our understanding of ACE2 expression in response to interferon. Accordingly, while there are broad strokes of agreement, there is not consensus on all points. Peer-review of the results needs to be first performed, and subsequent study is needed to validate and expand these new findings. In summary, at present, there is evidence that both short and long forms of ACE2 are upregulated by interferon, however the short form appears to be more strongly upregulated, appears to be expressed at higher levels in nose and mouth, and lacks the domains currently understood to bind the SARS-CoV-2 spike protein. Our results show significant predictions for interferon response elements in the proximal promoters of all three ACE2 transcripts. Further deepening the importance of understanding the role of interferon in COVID-19 is a new study showing that the presence of auto-antibodies against either or both of interferon alpha (IFNɑ) and interferon omega (IFNω) were rare in healthy, asymptomatic, or mild SARS-CoV-2 infection, but over-represented among patients with life-threatening COVID-19 pneumonia [82]. The full story of the regulation of ACE2 expression remains an enigma and there appear to be many factors involved. One limitation of our study is that it is focused on mRNA expression and transcriptional regulation only. There may exist factors which function at posttranscriptional level. Indeed, Srivastava and colleagues recently demonstrated that SARS-CoV-2 infection induces alterations in the post-transcriptional regulatory networks in human tissues through the function of RNA binding proteins and micro-RNAs [83]. There has been clinical concern that the use of ACE inhibitors and angiotensin receptor blockers could increase the expression of ACE2 and increase patient susceptibility to viral host cell entry [84, 85]. Previous studies have suggested that both ACE inhibitor and angiotensin II receptor type I antagonist therapies increase ACE2 mRNA expression in rat heart [86]. There has also been some evidence in humans showing increased expression of ACE2 in the heart, brain, and even in urine after treatment with angiotensin receptor blockers [84]. Since these drugs are widely used for treatment of hypertension and heart failure, it would be important to determine in COVID-19 patients whether these medications have any significant effects on symptoms or outcome of the disease.

Gene ontology investigations revealed interesting novel data on potential physiological roles of ACE2. The five most significant gene ontology terms included angiogenesis, blood vessel morphogenesis, vasculature development, cardiovascular system development, and blood vessel development. Angiotensin-(1–7) is a direct product of ACE2, and through binding with the Mas receptor has been shown to advance angiogenesis in injured cardiac tissue (myocardial infarction), by increasing expression of VEGF-D and MMP-9 [87], and in stroke [88]. Other studies have suggested that ACE2, either by reducing angiotensin II or through activities of the ACE2/angiotensin-(1–7)/MasR axis, may be negatively associated with angiogenesis in various cancers [81, 89–91]. It also appears to play a role in angiogenesis in uterus during pregnancy [92]. Our study of scRNA-Seq data from human lung showed that the arterial and venous vascular endothelial cell types had the highest ratios of ACE2-expressing cells, first and third highest, respectively. In another study, ACE2 expression was detected in blood vessels [27], while a recent study showed that SARS-CoV-2 is capable of directly infecting blood vessel cells [12]. Endothelial ACE2 expression may be linked to clotting and multi-organ dysfunction reported in many patients with COVID-19 [93]. Our GO analysis provided evidence that ACE2 is involved in the KEGG pathway 'complement and coagulation cascades'. Indeed, patients with severe COVID-19 often present with coagulation abnormalities that mimic other known systemic coagulopathies, such as disseminated intravascular coagulation (DIC) or thrombotic microangiopathy, but COVID-19 has its own distinct features [94]. Based on the present finding, angiogenesis/blood vessel morphogenesis may be considered another putative function for ACE2 in addition to its classical role as the key angiotensin-(1–7) forming enzyme [95].

Conclusions

Our bioinformatics study confirmed the low expression of ACE2 in the respiratory tract. In lung it was lowest of all, while significantly higher in the trachea. Bulk RNA-Seq analyses indicated the highest expression levels in the small intestine, colon, testis, and kidney. In the human lung scRNA-Seq dataset, the strongest positive signals for ACE2 mRNA were observed in vascular endothelial cells, goblet cells, ciliated cells, and AT2 and AT1 pneumocytes. In the mouse trachea dataset, positive signals were most common in club cells, goblet cells and ciliated epithelial cells. The results suggest that SARS-CoV infection may target the cell types that are important for the protection of airway mucosa and their damage may lead to deterioration of epithelial cell function, finally leading to a more severe lung disease with accumulation of alveolar exudate and inflammatory cells and lung edema, the signs of pneumonia recently described in the lung specimens of two patients with COVID-19 infection [96]. Gene ontology analysis based on expression in all tissues suggested that ACE2 is involved in angiogenesis/blood vessel morphogenesis processes in addition to its classical function in renin-angiotensin system. Many findings reported here have not yet been verified in vivo or in vitro. Therefore, the validity of the bioinformatics results needs to be verified by future experimental research.

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21 Jul 2020

PONE-D-20-16903

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

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Reviewer #1: 1. Figure 3 & 4: is not clear, the image quality is not good and different cell types is not readable.

2. What is the reason behind using both human and mouse tissues for the study? was there any difference between the ACE2 expression in mouse and human in different organs ? Please comment.

3. The results in the current paper with respect to ACE2 mRNA expression was contrasting with Lukassen and colleague, as they claimed high expression of ACE2 in human respiratory tract, which is the reason behind COVID19 patients experiencing pneumonia. Please explain.

4. Page 21, line 56-71, the authors conclude the high expression of ACE2 in Club, goblet and ciliated cells of trachea, which can cause respiratory distress, but the overall expression of ACE2 in figure 1 is highest in small intestine. What could be the reason behind the COVID19 patients not experiencing the gastrointestinal symptom as seen in SARs-CoV-1. The Club, goblet and ciliated cells are also expressed in the intestine. What could be the reason behind COVID19 infection severely affecting lungs?

5. The authors concluded, no difference in ACE2 expression was observed between the age groups and genders. Please reconcile with COVID19 reports adversely infecting older population and men.

6. The authors identified the possible transcription factor binding site, which drive ACE2 expression in lung and intestine. The figure 9 showed MGST1, MTUS1, and MGAM could be potential site, which needs to be discussed further. As I see the discussion part is highlighting the findings from other papers.

7. Expression of ACE2 in the current study does not match original reports of ACE2 expression (Harmer et al. 2002). Please explain.

Reviewer #2: In this paper, a secondary data analysis on human and mouse data sets was performed in regard to ACE2 mRNA expression using bioinformatics tools. The authors attempted to determine cell-specific expression of ACE2 mRNA in a number of organs that could likely be viewed as for SARS-Cov-2 virus entry ports. Prediction of transcription factor binding sites, gene ontology and sex-comparative analysis were also performed. The authors hypothesized that using these bioinformatic tools one could predict functions and transcriptional regulation of ACE2 yet not all approaches had been shown to be validated yet (see below).

The introduction clearly delineates the rationale for the studies performed. The Discussion is well structured and gives a logical summary of the presented findings. Less organized is the Results section. The data presented here also reveals some rigor deficiencies which makes the paper less convincing. Also, the approach has to be critically viewed by cautioning that other levels than transcriptional could be operative in regulating expression of ACE2.

In summary, the paper provides some interesting data and conclusions. However, there are deficiencies that need to be addressed.

Specific comments:

• Many studies, and also the Protein Atlas website, a tool the authors used themselves, have reported high ACE2 protein expression in the kidney yet the ACE2 mRNA expression in the kidney was reported here as relatively low. By unsophisticated comparison, there is a discrepancy that might suggest a level of regulation beyond the transcription. Moreover, there have been reports on post-transcriptional regulation of ACE2, e.g. in diabetes. It should be stated that one of the limitations of the approach used is that it is based on mRNA expression and transcriptional regulation only and that other levels of ACE2 regulation could be effective as well.

• The novel computational tool that has been recently developed by the first author of this article has not been published. Yet the authors use it to predict transcriptional regulation. Has this tool been validated in any way? How this tool works and how was it verified that the tool really works. If the tool has not been published and no data on its validation has been provided, should the reader just take it for granted? Some information has to be given on this or, if already available, the appropriate reference cited.

• Given that many of the findings have not yet been verified in vivo or in vitro, it would be appropriate to mention that the validity for the majority of the findings presented here would need to be verified by future research.

• In this paper, a secondary data analysis on human and mouse data set studies was performed. It takes a bit effort for the reader to find out what was studied. Given that various sources that were used for this, it would be useful to give some overview in form of a table or so: the source of data set, species, what was studied.

• There is insufficient information how the authors performed evaluation of the images that can be accessed from the Protein Atlas. How many images assessed, what magnification etc.

• Lanes 119 and 125 - Information is missing on what is GTEx database and where can be found.

• Is the expression of ACE2 in the kidney moderate (Abstract, Figure 2) of one of the highest (Discussion, Conclusions)?

Reviewer #3: In their manuscript, Barker and Parkkila investigate the expression and transcriptional regulation of ACE2, the receptor of SARS-CoV-2, through the analysis of existing datasets. Specifically, they determine the tissue and cell type specificity, investigate the covariates age and gender, and identify putative transcriptional regulators of ACE2 as well as genes with highly correlated expression patterns.

The manuscript is generally well written and nicely merges different layers of information on the expression and potential role of ACE2. The description of statistical tests is thorough, and appropriate tests are used for the gender and correlation analyses. For the analysis of age dependency, the one-way ANOVA implemented in scipy.stats is not an optimal choice as the ranking information of the age bins is lost. Ideally, the authors should try to work with the raw values if available and perform a regression fit here. Looking at the box plots, this should work reasonably well even with simple linear regression for most tissues, with the exception of kidney. Parts of the manuscript would benefit from a more detailed description of the approach taken, and some results contradict prior literature which would need to be addressed. Furthermore, as ACE2 is discussed specifically in its function as SARS-CoV-2 receptor, the manuscript could benefit from further analyses geared more towards ACE2 regulation in the context of an infection. I would not consider the latter point to be strictly necessary, provided the authors clearly state that their analysis is a representation of the physiological expression pattern and may not fully hold during a SARS-CoV-2 infection, but if it can be done quickly it would be worth considering.

Specific comments:

• It is sometimes unclear which dataset an analysis was performed on. In general, a structured list of datasets including links to the resources in the Methods section or as a supplementary table would be a great help.

• For the TFBS analysis, I’m not sure what the tissues in parentheses (e.g. lines 325/326) indicate. Was a correlation observed in these tissues (if yes, was the dataset bulk or single-cell)?

• It remains somewhat unclear how TFBS footprinting works. Based on the GitHub repository it mostly seems to aggregate PWM scores with experimental measures for TF occupancy. Given that there is no paper or preprint yet, the results presented in this manuscript should be explained in more detail (e.g., what data is the ATAC-Seq track based on? Is it lung or some other organ/aggregate database?). In addition, some corroborating evidence, or a rationale for why this is not needed, i.e. the data shown can directly be obtained from a database (which?), should be provided.

• ACE2 has been described as an interferon responsive gene. This could have implications when transferring the results presented here to the situation in COVID-19, as no major interferon signaling would be expected in healthy tissues. This could also help explain the disparity between the severity of COVID-19 and the lack of ACE2 expression in the lung beyond the initial infection. Several STAT/IRF TFs appear to have a significant hit according to the PWM scoring (and in the case of STAT1 for combined affinity). Since this interferon-responsiveness has now been found by several studies, it should be discussed, or directly addressed through inclusion of an infected dataset. Ideally that would be one of the COVID-19 datasets slowly becoming available, but if the question is just about interferon response, other infections such as influenza should work as well.

• The findings on the influence of age and gender contradict some existing literature on the subject (Muus et al., bioRxiv 2020; Bunjavanich et al., JAMA 2020; indirectly as they measured activity rather than expression: Soro-Paavonen et al., Journal of Hypertension 2012; Fernández-Atucha et al., Biology of Sex Differences 2017). While I wouldn’t necessarily consider the issue settled yet given the low expression of ACE2 and associated high levels of noise, these disparities should definitely be addressed.

• As ACE2 is rather strongly expressed in the vasculature, I would have been surprised to not see angiogenesis pop up as enriched pathway. While I wouldn’t discount the possibility that ACE2 does indeed play a role in this process, it will be hard to reliably make this point based on the datasets included in this manuscript. While the authors could flesh out their analysis with angiogenesis datasets, ideally showing a differential and stage-specific expression of ACE2 in the process on the RNA and/or protein level. There is a paper describing an ACE2 knockout mouse (Crackower et al., Nature 2002), but the phenotype they describe seems to hint more towards the “canonical” functions of ACE2.

• Fernández-Atucha et al. describe an age-dependent effect on ACE2 activity in females only (by linear regression). It might be worth looking at the interaction term rather than performing a one-way ANOVA here.

Minor comments:

The citation of the primary publication of the human protein atlas appears to be missing (see https://www.proteinatlas.org/about/licence).

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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4 Sep 2020

We thank the reviewers for their valuable comments. Our responses are in the attachment.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

18 Sep 2020

PONE-D-20-16903R1

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

PLOS ONE

Dear Dr. Parkkila,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the point raised by reviewer 3 refering to the newly discovered isoform of human ACE2.

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Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #2: Yes

Reviewer #3: Yes

**********

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Reviewer #3: Yes

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Reviewer #2: (No Response)

Reviewer #3: The authors have completely and thoroughly addressed my concerns, however, I cannot recommend acceptance of the article yet.

I am deeply sorry for raising another issue that wasn't included in the first round of revisions, but days after the decision on the previous version of the manuscript, three preprints (https://www.biorxiv.org/content/10.1101/2020.07.24.219139v1, https://www.biorxiv.org/content/10.1101/2020.07.19.210955v1, https://www.biorxiv.org/content/10.1101/2020.07.31.230870v1) were uploaded to bioRxiv that shed more light on the interferon-dependent regulation of ACE2, independently identifying a previously unknown, likely non-functional isoform of ACE2 that is strongly interferon-responsive. This isoform stems from an alternative promoter, leading to a 5' truncation that is not picked up by 3' (single-cell) RNA-Seq. As this is highly relevant to the question of regulation of ACE2 during COVID-19, I would strongly urge the authors to add an analysis of this alternative promoter. Furthermore, it might be worth looking at GTEx exon-level data at least for tissues where some sort of differential expression is observed, or restrict the comparison to the 5' exons to capture just the full-length isoform. If no major differences are observed, this information could be moved to a supplementary figure.

For this reason, I recommend a major revision. Please note that this is not reflective of the general quality of the work: Had this additional information not been made available in the meantime, I would have happily recommended acceptance of the article as-is.

Minor comment:

The quality of some figures is still rather bad. I think I had a similar issue with Editorial Manager in the past, where the uploaded high quality images were compressed too strongly. If that is the case, everything is fine. If the images in the reviewer file are reflective of the actual quality though, I would urge a shift to vector graphics or at least higher resolution images.

**********

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Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

28 Sep 2020

Reviewer #3:

Reviewer #3: The authors have completely and thoroughly addressed my concerns, however, I cannot recommend acceptance of the article yet. I am deeply sorry for raising another issue that wasn't included in the first round of revisions, but days after the decision on the previous version of the manuscript, three preprints (https://www.biorxiv.org/content/10.1101/2020.07.24.219139v1, https://www.biorxiv.org/content/10.1101/2020.07.19.210955v1, https://www.biorxiv.org/content/10.1101/2020.07.31.230870v1) were uploaded to bioRxiv that shed more light on the interferon-dependent regulation of ACE2, independently identifying a previously unknown, likely non-functional isoform of ACE2 that is strongly interferon-responsive. This isoform stems from an alternative promoter, leading to a 5' truncation that is not picked up by 3' (single-cell) RNA-Seq. As this is highly relevant to the question of regulation of ACE2 during COVID-19, I would strongly urge the authors to add an analysis of this alternative promoter. Furthermore, it might be worth looking at GTEx exon-level data at least for tissues where some sort of differential expression is observed, or restrict the comparison to the 5' exons to capture just the full-length isoform. If no major differences are observed, this information could be moved to a supplementary figure.

For this reason, I recommend a major revision. Please note that this is not reflective of the general quality of the work: Had this additional information not been made available in the meantime, I would have happily recommended acceptance of the article as-is.

OUR RESPONSE: We thank the reviewer for this valuable comment. We have revised the manuscript according to the new information. We have added the following texts and other materials (lines correspond to the version with ”track changes”:

Abstract, lines 37-38

Methods, lines 165-166

Results, lines 360-375

Discussion, lines 125-162

New Figure 9 has been added

References 56, 57, 58, and 82 have been added

Supplementary Table 5 has been revised

MINOR COMMENT: The quality of some figures is still rather bad. I think I had a similar issue with Editorial Manager in the past, where the uploaded high quality images were compressed too strongly. If that is the case, everything is fine. If the images in the reviewer file are reflective of the actual quality though, I would urge a shift to vector graphics or at least higher resolution images.

OUR RESPONSE: The figure quality has been poor because of the automated compression for the reviewer version of the manuscript. It shouldn´t be an issue for the final version of the paper.

OTHER MINOR CHANGES:

Gene abbreviations are written in Italic.

Supplementary Figure 1 has been updated to include STAT dimers which had been accidentally left out in the first analysis.

The number of COVID cases has been updated (lines 56-57).

Submitted filename: Response to reviewer_2.docx

Click here for additional data file.

1 Oct 2020

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

PONE-D-20-16903R2

Dear Dr. Parkkila,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Michael Bader

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

5 Oct 2020

PONE-D-20-16903R2

Bioinformatic characterization of angiotensin-converting enzyme 2, the entry receptor for SARS-CoV-2

Dear Dr. Parkkila:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

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Academic Editor

PLOS ONE