bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.04.22.056127

Heterogeneous expression of the SARS-Coronavirus-2 receptor ACE2 in the human respiratory tract

Abstract

BackgroundZoonotically transmitted coronaviruses are responsible for three disease outbreaks since 2002, including the current COVID-19 pandemic, caused by SARS-CoV-2. Its efficient transmission and range of disease severity raise questions regarding the contributions of virus-receptor interactions. ACE2 is a host ectopeptidase and the receptor for SARS-CoV-2. Numerous reports describe ACE2 mRNA abundance and tissue distribution; however, mRNA abundance is not always representative of protein levels. Currently, there is limited data evaluating ACE2 protein and its correlation with other SARS-CoV-2 susceptibility factors. Materials and methodsWe systematically examined the human upper and lower respiratory tract using single-cell RNA sequencing and immunohistochemistry to determine receptor expression and evaluated its association with risk factors for severe COVID-19. FindingsOur results reveal that ACE2 protein is highest within regions of the sinonasal cavity and pulmonary alveoli, sites of presumptive viral transmission and severe disease development, respectively. In the lung parenchyma, ACE2 protein was found on the apical surface of a small subset of alveolar type II cells and colocalized with TMPRSS2, a cofactor for SARS-CoV2 entry. ACE2 protein was not increased by pulmonary risk factors for severe COVID-19. Additionally, ACE2 protein was not reduced in children, a demographic with a lower incidence of severe COVID-19. InterpretationThese results offer new insights into ACE2 protein localization in the human respiratory tract and its relationship with susceptibility factors to COVID-19. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrevious studies of ACE2 mRNA transcript abundance in the human respiratory tract have suggested a possible association between ACE2 expression and age, sex, and the presence of comorbidities. However, these studies have provided conflicting results, as well as a lack of protein validation. Previous ACE2 protein studies have been limited by a paucity of lung tissue samples and reports that have produced contradictory results. Added value of this studyUsing a combination of single-cell RNA sequencing and immunohistochemistry, we describe ACE2 expression in the human respiratory tract. Staining protocols were optimized and validated to show consistent apical localization and avoid non-specific staining. We show ACE2 protein is found in subsets of airway cells and is highest within regions of the sinonasal cavity and pulmonary alveoli, sites of presumptive viral transmission and severe disease development for COVID-19, respectively. We show age, sex, and comorbidities do not increase ACE2 protein expression in the human respiratory tract. Implications of all the available evidenceACE2 protein abundance does not correlate with risk factors for severe clinical outcomes, but in some cases showed an inversed relationship. Features driving COVID-19 susceptibility and severity are complex, our data suggests factors other than ACE2 protein abundance as important determinants of clinical outcomes.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ortiz Bezara, M. E., Thurman, A., Pezzulo, A., Leidinger, M. R., Klesney-Tait, J. A., Karp, P. H., Tan, P., Wohlford-Lenane, C., McCray, P. B., Meyerholz, D. K.. 2020-04-23. Heterogeneous expression of the SARS-Coronavirus-2 receptor ACE2 in the human respiratory tract. https://doi.org/10.1101/2020.04.22.056127

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The yeast Gcn2 protein kinase mediates P-body formation both through its activity with eIF2α and with non-canonical substrates

P-bodies (PBs) are control hubs for RNA processing and storage that form when translation initiation is inhibited, including during stress. In yeast, Gcn2 is the sole eIF2 alpha kinase that inhibits translation initiation in response to diverse stresses. We show that PB formation depends strongly on Gcn2 during amino acid starvation but only partially during oxidative stress. Constitutively active Gcn2 also induces PB formation in unstressed cells. Surprisingly, PBs are still constitutively formed in a gcn3 mutant strain that is insensitive to eIF2 alpha phosphorylation suggesting that Gcn2 also promotes PB formation independently of eIF2 alpha phosphorylation. Analysis of several putative Gcn2-substrates involved in RNA metabolism identified several proteins required for Gcn2-mediated PB formation. Among these, Smu2, which was not previously implicated in PB formation, localises to PBs and is required for stress-induced coalescence of mRNA granules into PBs. Together, our findings indicate that Gcn2 promotes PB formation via two complementary routes: eIF2 alpha phosphorylation, which generates untranslated mRNAs, and Smu2-dependent maturation of mRNA granules into PBs.

cell biology↗

Quantitative Head-to-Tail Analysis Reveals Heterogeneous Cellular Organization and Connexin Expression in the Porcine Sinoatrial Node

Background: The sinoatrial node (SAN) is an integral component of the cardiac conduction system that dictates heart rhythm. Optical mapping studies have demonstrated functional differences between the superior and inferior leading pacemaking sites in the human SAN; however, the mechanistic connections between structure and function in the SAN through intra-tissue and inter-tissue current source-sink relationships have not been fully resolved. Methods: Porcine SAN longitudinal cryosections along the head-to-tail axis were immunostained and analyzed for regional cell organization and connexin (CX) isoform expression. Results: Quantification of pacemaking cardiomyocyte (PCM) density revealed >9-fold more PCMs within a SAN volume than suggested by a widely cited estimate, assuming comparable PCM densities across species. The SAN head region consisted of large PCM clusters with low aspect ratios, high PCM density, low fibroblast density, and PCMs with the highest HCN4 expression and lowest CX40 and CX43 expression. These characteristics are consistent with an electrically well-insulated, strong current-source that supports pacemaking function. Although the SAN tail also exhibited high local PCM density, its smaller PCM clusters, lower HCN4 expression, and higher CX40, CX43, and CX45 expression rendered it a weaker current source. The SAN mid-region exhibited large PCM clusters aligned in the head-to-tail axis and the highest expression of CX40 and CX45, an expression profile that suggests a fast-conducting intra-tissue pathway that electrically links the leading pacemaking sites at the head and tail. The peripheral SAN at the head and tail also exhibited higher CX expression of all isoforms than the central subregions, an organization consistent with enhanced inter-tissue coupling required for proper pacing function. Conclusions: Our data provide novel insights into the distinct regional micro-organization of the SAN along the head-to-tail axis, which through intra-tissue and inter-tissue coupling, likely modulates the balance of current source-sink relationships that supports robust pacemaking and pacing functions.

cell biology↗

Inference of cell division parameters in bacteria under transient growth conditions

Most quantitative models of bacterial cell division assume steady-state growth, yet natural environments are highly dynamic. Here, we infer division control parameters of E. coli from single-cell size trajectories under nonsteady growth. We describe growth as a continuous process interrupted by stochastic divisions, whose propensity depends on cell size and on the size added since birth. Combining an estimation of time-dependent single-cell growth rates with a maximum-likelihood estimator that we previously validated on synthetic data, we obtain time-resolved estimates of the mean added size at division, its noise, and a strategy parameter that measures how added size depends on birth size. This parameter vanishes for the adder, in which cells add, on average, a fixed size regardless of their size at birth, and is negative for sizer-adder control, in which larger-born cells add less size. We apply this approach to published single-cell data covering steady exponential growth, gradual nutrient depletion, and exit from starvation. We implement a time-resolved model selection based on the Akaike information criterion, concluding that the adder is sufficient during steady growth, whereas size dynamics during entry into and exit from stationary phase are better explained by transient deviations toward sizer-adder control. The framework provides a general method to quantify time-resolved changes in the scale, stochasticity, and strategy of division in nonsteady environments.

cell biology↗