ACE2-Expressing Endothelial Cells in Aging Mouse Brain

Su Bin Lim¹, Valina L. Dawson^1,2,3,4*, Ted M. Dawson^1,3,4,5* and Sung-Ung Kang^1*
*Correspondence: Valina L. Dawson, Department of Neurology, Johns Hopkins University School of Medicine, USA, Email: Ted M. Dawson, Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Email: Sung-Ung Kang, Department of Neuroregeneration and Stem Cell Programs, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA, Email:

Keywords

Brain • Aging • ACE2-expressing endothelial cells • Transcriptomic

Introduction

In addition to the well-known respiratory symptoms, COVID-19 patients suffer from a loss of smell and taste, headache, impaired consciousness, and nerve pain [1]. Raising possibility of virus infiltration in the nervous system, including brain. Despite cases emerging of COVID-19 patients with neurologic manifestations, potential neurotropic mechanisms underlying SARS-CoV-2-mediated entry into the cells of the brain are largely unexplored.

Evidenced by transgenic mice models [2,3]. The evolutionarily-related coronaviruses, such as SARS-CoV and MERS-CoV, can invade the brain by replicating and spreading through the nasal cavity, and possibly olfactory bulbs located in close proximity to the frontal lobes of the brain [4]. Once inside the brain, viruses can harm the brain directly and indirectly by infecting the cells and myelin sheaths, and by activating microglia, which may in turn consume healthy neurons to induce neuroinflammation and neurodegeneration [5].

Many of the observed neurological symptoms observed may in part be explained by a primary vasculopathy and hypercoagulability [6]. As endothelial dysfunction and the resulting clotting are increasingly being observed in patients with severe COVID-19 infection [7-9]. Consistent with these findings, the first pathologic evidence of direct viral infection of the EC and lymphocytic endotheliitis has been found in multiple organs, including lung, heart, kidney and liver, in a series of COVID-19 patients [10].

In contrast, recent clinical findings, including an MRI study [11]. And immunohistochemistry and RT-qPCR analyses [12,13]. Did not observe any signs of encephalitis from postmortem brain examination of COVID-19 patients. Similarly, postmortem analysis of SARS-CoV-2-exposed mice transgenically expressing ACE2 via mouse ACE2 promoter failed to detect the virus in the brain [14]. In light of such controversy regarding neuropathological features, a more comprehensive assessment on the distribution of ACE2 in a cell type-specific manner is required to identify putative brain host cells.

Here, we analyzed publicly available and spatially rich brain RNA-seq datasets to assess ACE2 distribution in mouse brains at the single-cell and single-nuclei level. We found that ACE2 was consistently expressed in small subpopulations of Endothelial Cells (ECs) and mural cells in all the analyzed datasets, in which the impaired blood-brain barrier was further implicated in the aged brains. These findings altogether may hold potential to initiate new avenues of research on specific cells types (EC and vascular mural cells) that remain poorly understood particularly in relation to the aging and viral infection in the brain.

Methods

Mouse brain sc/snRNA-seq data and analysis

Of all the independent studies retrieved with the term “ACE2” from the Single Cell Portal 11 sc/snRNA-seq datasets were derived from (1) adult (young or old) mouse brains, and had (2) author-defined cell type annotations including endothelial cells (EC, PC, or VSMC). A total of 801,658 cells were analyzed in this study, based on the organ (i.e., brain) and species of origin (i.e., mouse), and diseased status (i.e., normal). 2-D tSNE/UMAP plots (colored by cell type) and box plots for cell type-specific ACE2 expression presented in this study were generated by the Single Cell Portal. t-SNE visualization (colored by age) and 25 cell type-specific DE gene lists of the aging mouse brains (T1) were obtained from the advanced interactive data viewer Genes with logGER (Gene Expression Ratio) |>0.1 and FDR<0.05 were defined as DEGs, and were analyzed for functional pathway enrichment using GeneAnalyics.

Human brain bulk RNA-seq data and analysis

De-identified processed human brain bulk RNA-seq data and annotation files for sample attributes and subject phenotypes were obtained from the Genotype-Tissue Expression (GTEx) portal. Data from amygdala (n=88), anterior cingulate cortex (n=107), caudate (basal ganglia) (n=142), cerebellar hemisphere (n=121), cerebellum (n=138), hippocampus (n=123), frontal cortex (BA9) (n=124), cortex (n=137), hypothalamus (n=120), nucleus accumbens (basal ganglia) (n=140), putamen (basal ganglia) (n=121), substantia nigra (n=74) were analyzed in this study. A total of 1,435 samples were divided into two age groups: young (<60) and old (≥ 60 years old). Expression levels (in TPM) were compared between the two age groups for all genes (n=56,200) by t-test with multiple testing correction. The performance of Bonferroni correction and False Discovery Rate (FDR)-Benjamini-Hochberg (BH) procedure was assessed using the RStudio (Version 1.2.5019) base function adjust. The R qvalue package from Bioconductor was used to assess the performance of q-value approach [15]. The R biomaRt package from Bioconductor was used to convert mouse EC DEGs to human gene symbols (hgnc) using getLDS() function [16]. Gene Set Enrichment Analysis (GSEA v4.0.3; [17]. was used to assess EC DEGs (EC up- and down-regulated genes) in GTEx human brain bulk RNA-seq samples. Following parameters were set to run enrichment tests: (1) number of permutations=1,000, (2) collapse/remap to gene symbols=no_collapse, and (3) permutation type=gene_set.

Conclusion

We have compared expression levels (in TPM) of human orthologs of the aged EC DEGs matched in the GTEx data between the young (<60) and old (≥ 60 years) human samples. Of all genes evaluated (n=56,200), 8,215 (15%) showed the expression level significantly different (t-test q-value ≤ 0.05; see Methods) while the expression levels of 37% of EC DEGs were different in the old group from that of the young population, indicating concordant EC transcriptomic changes in normal aged human brains. This conclusion was further supported by Gene Set Enrichment Analysis (GSEA) results, displaying a significant enrichment of the aged (n=141) and young (n=133) mouse EC DEGs in the old (n=814) and young (n=621) human samples, respectively. In conclusion, we have identified specific EC signatures that are functionally important and related to the aging and viral infection in the brain.

Acknowledgements

This work was supported by grants from NIH/NIA AG059686. T.M.D. is the Leonard and Madlyn Abramson Professor in Neurodegenerative Diseases.

Author Contributions

V.L.D., T.M.D., and S.U.K. supervised the project; S.B.L., V.L.D., T.M.D. and S.U.K. formulated the hypothesis; S.B.L. and S.U.K. performed research; S.B.L., V.L.D., T.M.D., and S.U.K. wrote the paper.

Competing Interests

The authors declare no competing interest.

References

1. Ling, Mao, Huijuan Jin, Mengdie Wang and Yu Hu et al. “Neurologic Manifestations of Hospitalized Patients With Coronavirus Disease 2019 in Wuhan, China.” JAMA Neurol  77(2020): 683-690.
2. Jason, Netland, David Meyerholz K, Steven Moore and Martin Cassell et al. “Severe Acute Respiratory Syndrome Coronavirus Infection Causes Neuronal Death in the Absence of Encephalitis in Mice Transgenic for Human ACE2.” J virol 82(2008): 7264-7275.
3. Li,Kun, Christine Wohlford-Lenane, Stanley Perlman and Jincun Zhao et al. “Middle East Respiratory Syndrome Coronavirus Causes Multiple Organ Damage and Lethal Disease in Mice Transgenic for Human Dipeptidyl Peptidase 4.” J Infect Dis 213(2016): 712-722.
4. Li, Yan Chao, Wan Zhu Bai and Tsutomu Hashikawa. “The Neuroinvasive Potential of SARS-CoV2 May Play a Role in the Respiratory Failure of COVID-19 Patients.” J Med Virol 92(2020): 552-555.
5. Haeman, Jang, David Boltz, Katharine Sturm-Ramirez and Kennie R Shepherd et al. “Highly Pathogenic H5N1 Influenza Virus Can Enter the Central Nervous System and Induce Neuroinflammation and Neurodegeneration.” Proc Natl Acad Sci USA 106(2009): 14063.
6. Keiland W, Cooper, David H Brann, Michael C Farruggia and Surabhi Bhutani et al. “COVID-19 and the Chemical Senses: Supporting Players Take Center Stage.” Neuron 107(2020): 219-233
7. Klok, FA, M J H A Kruip, van der Meer N J M and Arbous M S et al. “Incidence of Thrombotic Complications in Critically Ill ICU Patients with COVID-19.” Throm res 191(2020): 145-147.
8. Fei Zhou, Ting Yu, Ronghui Du and Guohui Fan et al. “Clinical Course and Risk Factors for Mortality of Adult Inpatients with COVID-19 in Wuhan, China: A Retrospective Cohort Study.” Lancet 395(2020): 1054-1062.
9. Yan, Zhang, Meng Xiao,  Shulan Zhang and Peng Xia et al. “Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19.” n engl j med 382(2020): e38.
10. Zsuzsanna, Varga, Andreas J Flammer, Peter Steiger and Martina Haberecker et al. “Endothelial Cell Infection and Endotheliitis in COVID-19.” Lancet 395(2020): 1417-1418
11. Tim, Coolen, Valentina Lolli, Niloufar Sadeghi and Antonin Rovai et al. “Early Postmortem Brain MRI Findings in COVID-19 Non-Survivors.” Neurology 95 (2020).
12. Tina, Schaller, Klaus Hirschbühl, Katrin Burkhardt and  Georg Braun et al. “ Postmortem Examination of Patients With COVID-19.” Jama 323(2020): 2518-2520.
13. Isaac H, Solomon, Erica Normandin, Shamik Bhattacharyya and Shibani S. Mukerji et al. “Neuropathological Features of Covid-19.” N Engl J Med 383(2020): 989-992.
14. Linlin, Bao, Wei Deng, Baoying Huang and Hong Gao et al. “The Pathogenicity of SARS-CoV-2 in hACE2 Transgenic Mice.” Nature 583(2020): 830–833.
15. Luc, Bertrand, Hyung Joon Cho and Michal Toborek. “Blood–Brain Barrier Pericytes as a Target for HIV-1 Infection.” Brain 142(2019): 502-511.
16. Turgay Dalkara, Yasemin Gursoy-Ozdemir and Muge Yemisci. “Brain Microvascular Pericytes in Health and Disease.” Acta neuropathologica 122(2011): 1-9.
17. GTEx, Consortium. “Genetic Effects on Gene Expression Across Human Tissues.” Nature 550(2017): 204-213.
18. David H, Brann, Tatsuya Tsukahara, Caleb Weinreb and Marcela Lipovsek et al. “Non-Neuronal Expression of SARS-CoV-2 Entry Genes in the Olfactory System Suggests Mechanisms Underlying COVID-19-Associated Anosmia.” bioRxiv (2020).
19. Leon, Fodoulian, Joel Tuberosa, Daniel Rossier and Madlaina Boillat et al. “SARS-CoV-2 Receptor and Entry Genes are Expressed by Sustentacular Cells in the Human Olfactory Neuroepithelium.” bioRxiv (2020).
20. Christoph, Muus, Malte D Luecken, Gokcen Eraslan and Avinash Waghray et al. “Integrated Analyses of Single-Cell Atlases Reveal age, Gender, and Smoking Status Associations with Cell Type-Specific Expression of Mediators of SARS-CoV-2 Viral Entry and Highlights Inflammatory Programs in Putative Target Cells.” bioRxiv (2020).
21. Rongrong, Chen, Keer Wang, Jie Yu and Zhong Chen et al. “The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in Human and Mouse Brain.” bioRxiv (2020).
22. Liuliu, Yang, Yuling Han, Benjamin E Nilsson-Payant and Vikas Gupta et al. “A Human Pluripotent Stem Cell-based Platform to Study SARS-CoV-2 Tropism and Model Virus Infection in Human Cells and Organoids.” Cell stem cell  125(2020): 136 e127.
23. Korin, Bullen, Helena T Hogberg, Asli Bahadirli Talbott and William R. Bishai et al. “Infectability of Human BrainSphere Neurons Suggests Neurotropism of SARS-CoV-2.” Altex 37(2020).
24. John D, Storey, Andrew J Bass,  Alan Dabney and David Robinson et al. “Q-value Estimation for False Discovery Rate Control.” Rdrr.io (2020).
25. Steffen, Durinck, Paul T Spellman, Ewan Birney and Wolfgang Huber. “Mapping Identifiers for the Integration of Genomic Datasets with the R/Bioconductor Package BiomaRt.” Nature 4(2009): 1184-1191.
26. Aravind, Subramanian, Pablo Tamayo, Vamsi K Mootha and Sayan Mukherjee  et al. “Gene Set Enrichment    Analysis: a Knowledge-based Approach for Interpreting Genome-wide Expression Profiles.” PNAS 102(2005): 15545-15550.