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Meta-Analysis
. 2021 Dec;16(4):756-769.
doi: 10.1007/s11481-021-10012-9. Epub 2021 Nov 10.

Network Meta-analysis on the Changes of Amyloid Precursor Protein Expression Following SARS-CoV-2 Infection

Ryan C Camacho #  1 , Sedra Alabed #  1   2 , Heping Zhou  3 , Sulie L Chang  4   5
Affiliations
Meta-Analysis

Network Meta-analysis on the Changes of Amyloid Precursor Protein Expression Following SARS-CoV-2 Infection

Ryan C Camacho et al. J Neuroimmune Pharmacol. 2021 Dec.

Abstract

SARS-CoV-2 infection begins with the attachment of its spike (S) protein to angiotensin-converting enzyme-2 (ACE2) followed by complex host immune responses with cardiovascular and neurological implications. Our meta-analyses used QIAGEN Ingenuity Pathway Analysis (IPA) and Knowledge Base (QKB) to investigate how the expression of amyloid precursor protein (APP) was modulated by attachment of SARS-CoV-2 S protein in the brain microvascular endothelial cells (BMVECs) and during COVID-19 in progress. Published 80 host response genes reported to be modulated in BMVECs following SARS-CoV-2 S protein binding were used to identify key canonical pathways and intermediate molecules mediating the regulation of APP production following the attachment of S protein to endothelial cells. This revealed that the attachment of SARS-CoV-2 S protein may inhibit APP expression in the BMVECs. Our results shed light on the molecular mechanisms by which SARS-CoV-2 infection may potentiate the incidence of stroke by inhibiting the production of APP in the BMVECs. We also analyzed molecules associated with COVID-19, which revealed six upstream regulators, TNF, IFNG, STAT1, IL1β, IL6, and STAT3. The upstream regulators mediate the increased production of APP via intermediators, with eleven regulated by all six upstream regulators. These COVID-19 upstream regulators increased APP expression with a statistically significant Z-score of 3.705 (p value = 0.000211). These findings have revealed molecular mechanisms by which COVID-19 disease may lead to long-term neurological manifestations resulting from the elevated APP expression in line with immune response in the host. Altogether, our study revealed two distinct scenarios which may have differential impact on APP expression.

Keywords: Alzheimer’s disease; Blood–brain barrier; Brain microvascular endothelial cells; COVID-19; Neuroinflammation.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The identification process and composition of the 165 molecules connecting the 80 genes modulated by the binding of SARS-CoV-2 S protein and APP. A Flow chart of the stepwise process to identify the 165 molecules associated with the 80 genes modulated by the binding of SARS-CoV-2 S protein to BMVEC, and also influence APP expression; B Venn diagram displaying the makeup of the 165 genes with respect to the 12 upregulated genes and 68 downregulated genes
Fig. 2
Fig. 2
Top 15 canonical pathways in the network of all known connections between the 80 genes modulated by binding of SARS-CoV-2 to BMVEC and APP
Fig. 3
Fig. 3
Potential pathways connecting the 12 upregulated and 68 downregulated genes in BMVEC following SARS-CoV-2 S protein attachment and APP. A “MAP” simulated activation of the 12 genes according to their relative expression fold changes, leading to increased APP production directly or indirectly via 68 intermediary molecules; B “MAP” simulated decrease of the 68 genes according to their relative expression fold changes, leading to decreased APP production directly or indirectly via 158 intermediary molecules
Fig. 4
Fig. 4
Influence of the 80 SARS-CoV-2 S-protein-modulated genes in BMVEC on APP expression. “MAP” simulated modulation of the 80 genes according to their relative expression fold changes, resulted in the inhibition of APP expression. This inhibition occurred through 165 intermediary molecules, represented in the figure through the intermediary molecule nodes
Fig. 5
Fig. 5
Flow chart for the analysis of COVID-19’s impact on APP
Fig. 6
Fig. 6
Molecules affected by COVID-19 Upstream Regulators and APP. A TNF was revealed as an upstream regulator with the most significant p-value of 1.85E−28. There was a total of 44 intermediates, 13 of which were downstream from APP, 23 had no determined impact by TNF or on APP, and 8 were impacted by TNF and influenced APP expression; B IFNG was revealed as an upstream regulator with a p-value of 3.6E−27. There was a total of 39 intermediates, 10 of which were downstream from APP, 19 had no determined impact by IFNG or on APP, and 11 were impacted by IFNG and influenced APP expression. C IL1B and IL6 were revealed as upstream regulators with a p-value of 3.23E−24 and 8.35E−22, respectively. There was a total of 39 intermediates between both, 10 of which were downstream from APP, 20 had no determined impact by IL1B and IL6, or on APP, and 9 were impacted by IL1B or IL6 and influenced APP expression. D STAT1 and STAT3 were revealed as upstream regulators with a p-value of 9.35E−27 and 1.15E−20, respectively. There was a total of 33 intermediates between both, 11 of which were downstream from APP, 16 had no determined impact by STAT1 or STAT3, or on APP, and 6 were impacted by STAT1 and STAT3 and influenced APP expression
Fig. 7
Fig. 7
The holistic impact of the 6 COVID-19 upstream regulators on APP expression and top 15 canonical pathways of the 11 intermediates. A After combining the intermediates of each of the upstream regulators into one pathway, the “MAP” tool was used to simulate the activation of TNF, IFNG, IL1B, IL6, STAT1, and STAT3. The resulting impact on the 11 intermediates led to an increase in the expression of APP; B The “Core Analysis” of the 11-molecule dataset, obtained from the overlapping molecules of COVID-19 upstream regulators and APP, revealed a list of canonical pathways in which they overlapped. The top canonical pathway was the Acute Phase Response Signaling, following by the Neuroinflammation Signaling pathway
Fig. 8
Fig. 8
Individual contribution of the 6 COVID-19 upstream regulators on APP expression. The overall change in APP expression by the upregulation of the 6 COVID-19 upstream regulators was measured by the individual involvement, z(r), of each of the 6 upstream regulators
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