Back

ACE2-expressing endothelial cells in aging mouse brain

SU Bin Lim; Valina L. Dawson; Ted M. Dawson; Sung-Ung Kang

2020-07-11 neuroscience
10.1101/2020.07.11.198770 bioRxiv
Show abstract

Angiotensin-converting enzyme 2 (ACE2) is a key receptor mediating the entry of SARS-CoV-2 into the host cell. Through a systematic analysis of publicly available mouse brain sc/snRNA-seq data, we found that ACE2 is specifically expressed in small sub-populations of endothelial cells and mural cells, namely pericytes and vascular smooth muscle cells. Further, functional changes in viral mRNA transcription and replication, and impaired blood-brain barrier regulation were most prominently implicated in the aged, ACE2-expressing endothelial cells, when compared to the young adult mouse brains. Concordant EC transcriptomic changes were further found in normal aged human brains. Overall, this work reveals an outline of ACE2 distribution in the mouse brain and identify putative brain host cells that may underlie the selective susceptibility of the aging brain to viral infection.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Aging Cell
○ 165 papers in training set
Top 0.2%
15.4%
Similar papers in this journal
2
GeroScience
○ 109 papers in training set
Top 0.2%
10.8%
Similar papers in this journal
3
Acta Neuropathologica Communications
○ 89 papers in training set
Top 0.3%
5.6%
Similar papers in this journal
4
Aging
○ 75 papers in training set
Top 0.3%
4.5%
Similar papers in this journal
5
Alzheimer's Research & Therapy
○ 57 papers in training set
Top 0.5%
3.6%
Similar papers in this journal
6
Molecular Psychiatry
○ 282 papers in training set
Top 2%
3.3%
7
Molecular Neurodegeneration
○ 55 papers in training set
Top 0.6%
3.3%
8
Journal of Biomedical Science
○ 17 papers in training set
Top 0.1%
3.2%
9
Journal of Neuroinflammation
○ 61 papers in training set
Top 0.4%
2.8%
50% of probability mass above
10
International Journal of Molecular Sciences
○ 494 papers in training set
Top 5%
2.5%
11
Nature Communications
○ 5641 papers in training set
Top 39%
2.4%
12
Frontiers in Aging Neuroscience
○ 74 papers in training set
Top 0.6%
2.4%
13
Alzheimer's & Dementia
177 papers in training set
Top 1%
2.0%
14
eLife
● 5828 papers in training set
Top 47%
1.9%
15
iScience
○ 1154 papers in training set
Top 18%
1.7%
16
Scientific Reports
○ 3612 papers in training set
Top 52%
1.7%
17
Cells
○ 249 papers in training set
Top 3%
1.5%
18
Cell Reports
○ 1498 papers in training set
Top 21%
1.4%
19
npj Aging
○ 22 papers in training set
Top 0.3%
1.1%
20
BMC Genomics
○ 406 papers in training set
Top 6%
1.1%
21
Communications Biology
○ 993 papers in training set
Top 20%
1.1%
22
PLOS ONE
● 5266 papers in training set
Top 51%
1.1%
23
Neurobiology of Aging
○ 107 papers in training set
Top 1%
1.0%
24
Journal of Alzheimer’s Disease
○ 50 papers in training set
Top 1%
1.0%
25
Journal of Alzheimer's Disease
○ 48 papers in training set
Top 0.9%
0.9%
26
Advanced Science
○ 286 papers in training set
Top 9%
0.9%
27
Nature Aging
○ 60 papers in training set
Top 2%
0.9%
28
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
0.6%
29
Human Molecular Genetics
◐ 141 papers in training set
Top 3%
0.6%
30
eneuro
● 439 papers in training set
Top 8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.