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Meta-Analysis
. 2022 Jan 21;23(3):1182.
doi: 10.3390/ijms23031182.

Meta-Analysis of APP Expression Modulated by SARS-CoV-2 Infection via the ACE2 Receptor

Alyssa Caradonna  1 , Tanvi Patel  1   2 , Matea Toleska  1 , Sedra Alabed  1   2 , Sulie L Chang  1   2
Affiliations
Meta-Analysis

Meta-Analysis of APP Expression Modulated by SARS-CoV-2 Infection via the ACE2 Receptor

Alyssa Caradonna et al. Int J Mol Sci. .

Abstract

Alzheimer's disease (AD) is characterized by the deposition of amyloid-beta (Aβ) plaques from improper amyloid-beta precursor protein (APP) cleavage. Following studies of inflammation caused by coronavirus-2019 (COVID-19) infection, this study investigated the impact of COVID-19 on APP expression. A meta-analysis was conducted utilizing QIAGEN Ingenuity Pathway Analysis (IPA) to examine the link between severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and the modulation of APP expression upon virus binding the Angiotensin-converting enzyme-2 (ACE2) receptor. A Core Analysis was run on the infection by severe acute respiratory syndrome (SARS) coronavirus node, which included molecules affected by SARS-CoV-2, revealing its upstream regulators. Intermediary molecules were found between the upstream regulators and ACE2 and between ACE2 and APP. Activation of the upstream regulators downregulated the expression of ACE2 with a Z-score of -1.719 (p-value = 0.086) and upregulated APP with a Z-score of 1.898 (p-value = 0.058), showing a less than 10% chance of the results occurring by chance and pointing to an inverse relationship between ACE2 and APP expression. The neuroinflammation signaling pathway was the fifth top canonical pathway involved in APP upregulation. The study results suggest that ACE2 could be downregulated by SARS-CoV-2, resulting in APP upregulation, and potentially exacerbating the onset and progression of AD.

Keywords: Alzheimer’s disease; amyloid-beta precursor protein; angiotensin-converting enzyme-2; coronavirus-2019; meta-analysis; severe acute respiratory syndrome-coronavirus-2.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Impact of IFNG on ACE2 and APP. When IFNG is upregulated, ACE2 is predicted to be inhibited. The inhibition of ACE2 led to the overall activation of APP.
Figure 2
Figure 2
Impact of TNF and CD40LG on ACE2 and APP. When TNF and CD40LG were upregulated, ACE2 was predicted to be inhibited. This inhibition of ACE2 led to the predicted overall activation of APP downstream.
Figure 3
Figure 3
Impact of IL1B, IL6, IL10, IL4, and IL1 on ACE2 and APP. When IL1B, IL6, IL10, IL4, and IL1 were upregulated, ACE2 was predicted to be inhibited. This inhibition of ACE2 led to the predicted overall activation of APP downstream.
Figure 4
Figure 4
Impact of Immunoglobulin on ACE2 and APP. When immunoglobulin is upregulated, ACE2 is predicted to be inhibited. This inhibition of ACE2 led to the overall very slight activation of APP.
Figure 5
Figure 5
Impact of STAT3 on ACE2 and APP. The upregulation of STAT3 led to the predicted activation of ACE2. This activation of ACE2 led to the overall inhibition of APP.
Figure 6
Figure 6
Impact of TLR3 on ACE2 and APP. The upregulation of TLR3 led to the predicted activation of ACE2. The activation of ACE2 led to the overall predicted inhibition of APP.
Figure 7
Figure 7
Impact of TGFB1 on ACE2 and APP. The upregulation of TGFB1 led to an overall predicted activation of ACE2. The activation of ACE2 led to the overall predicted inhibition of APP.
Figure 8
Figure 8
(A) Kramer values for ACE2 expression. This chart depicts the Kramer values calculated between each upstream regulator and ACE2. (B) Kramer values for contribution to APP expressionfor each upstream regulator.
Figure 9
Figure 9
Holistic impact of upstream regulators on ACE2 and APP. This pathway depicts the upregulation of all the upstream regulators leading to the predicted inhibition of ACE2. This inhibition of ACE2 led to the predicted overall activation of APP downstream.
Figure 10
Figure 10
The top 15 canonical pathways. The top 15 canonical pathways and their −log(p-value) values related to the pathways represent the overall impact of the upstream regulators.
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