RESEARCH ARTICLE

Full Access

Whole-transcriptome sequencing data reveals a disparate cognitive and immune signature in COVID-19 patients with and without dementia

Jue Yang

State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China

The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, China

Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, China

State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

Search for more papers by this author

Hui Song,

Corresponding Author

Hui Song

[email protected]

orcid.org/0000-0002-9204-0960

Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, China

Correspondence Xiaojiang Hao, State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Hui Song, Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Search for more papers by this author

Xiaojiang Hao,

Corresponding Author

Xiaojiang Hao

[email protected]

State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China

The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, China

Correspondence Xiaojiang Hao, State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Hui Song, Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Search for more papers by this author

Jue Yang,

Jue Yang

State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China

The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, China

Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, China

State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

Search for more papers by this author

Hui Song,

Corresponding Author

Hui Song

[email protected]

orcid.org/0000-0002-9204-0960

Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, China

Correspondence Xiaojiang Hao, State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Hui Song, Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Search for more papers by this author

Xiaojiang Hao,

Corresponding Author

Xiaojiang Hao

[email protected]

State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China

The Key Laboratory of Chemistry for Natural Products of Guizhou Province and Chinese Academic of Sciences, Guiyang, China

Correspondence Xiaojiang Hao, State Key Laboratory of Functions and Applications of Medicinal Plants, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Hui Song, Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University, Guiyang, China.

Email: [email protected]

Search for more papers by this author

First published: 27 September 2022

https://doi.org/10.1002/jmv.28177

Citations: 1

Share a link

Email
Wechat
Bluesky

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused more than 6.3 million deaths worldwide. Recent evidence has indicated that elderly people with dementia are particularly vulnerable to COVID-19 and severe disease outcomes. However, its molecular mechanism remains largely unknown. Here, we retrieved frontal cortex samples of COVID-19 patients from the Gene Expression Omnibus database and performed a systematic transcriptomic analysis to compare COVID-19 patients and controls with or without dementia. In nondemented patients, SARS-CoV-2 infection obviously activated T helper type 2 (Th2) cell-mediated humoral immunity and reduced the pathogenesis of dementia-related Alzheimer's disease and Parkinson's disease. In demented patients, conversely, SARS-CoV-2 infection significantly increased T helper type 1 (Th1) cell-mediated cellular immunity and exacerbated the progression of dementia-related diseases. We further analyzed the molecular characteristics of COVID-19 patients with and without dementia. Compared with nondemented COVID-19 patients, demented COVID-19 patients showed decreased enrichment scores of Calcium signaling pathway, Neuroactive ligand-receptor interaction, ABC transporters, and Peroxisome, and increased enrichment scores of Olfactory transduction and Regulation of autophagy. The ratio of Th1/Th2 cells was significantly increased from 1.17 in nondemented COVID-19 patients to 33.32 in demented COVID-19 patients. Taken together, our findings provide transcriptomic evidence that COVID-19 has distinct influences on cognitive function and immune response in patients with and without dementia.

1 INTRODUCTION

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has rapidly spread to become a pandemic. According to the World Health Organization (WHO), as of June 30, 2022, more than 543.3 million cases and 6.3 million deaths have occurred worldwide.¹ Elderly people are considered more susceptible to COVID-19 and have higher mortality than any younger age group.^2-5 Limited data suggest that SARS-CoV-2 can enter the central nervous system and preferentially target the frontal lobes.^6-8 For both vaccinated and unvaccinated patients, pre-existing dementia increased the risk of COVID-19-related hospitalization and death by more than twofold.^9-11 Hence, it is urgent to understand the molecular mechanism underlying high mortality caused by COVID-19 in demented patients.

Dementia is a syndrome characterized by progressive cognitive impairment and ranks as a leading cause of disability among older people worldwide.¹² According to estimates from the WHO, the number of people living with dementia is expected to double over the next decades, from 78 million in 2030 to 139 million in 2050.¹³ Alzheimer's disease is the most common cause of dementia, accounting for an estimated 60%–80% of cases. Other common causes of dementia include cerebrovascular disease, frontotemporal lobar degeneration, and Parkinson's disease.¹⁴ Extensive studies have provided clear evidence that dementia significantly increases the SARS-CoV-2 infection risk and COVID-19-related mortality, indicating heterogeneity and a distinct molecular pattern between patients with and without dementia.^15-17 However, few studies have paid full attention to the difference between the demented and nondemented cases during COVID-19.

To explore the possible difference between the demented and nondemented patients with COVID-19 and the potential mechanisms, we retrieved two public cohort datasets from the Gene Expression Omnibus (GEO) database, and characterized the transcriptomic changes in the frontal cortex tissues of COVID-19 patients and controls with or without dementia. Our data revealed a disparate influence of COVID-19 on cognitive function and immune response between patients with and without dementia and provided a possible link between COVID-19 and dementia by regulating the inflammation.

2 MATERIALS AND METHODS

2.1 Data source

Two COVID-19-related human frontal cortex expression profiling datasets, GSE188847 and GSE164332, were downloaded from the GEO database. GSE188847 data set comprised 12 COVID-19 patients and 12 age-matched controls (mean age 66.6 years old), with no known psychiatric or neurological disorders (platform: GPL24676; Illumina NovaSeq 6000).¹⁸ GSE164332 data set included eight COVID-19 patients (six patients with dementia and two patients without dementia) and 6 controls with dementia (mean age 83.8 years old) (platform: GPL18573; Illumina NextSeq 500), and the dementia status was evaluated by a forensic medical doctor and a neurologist.¹⁹ The clinical characteristics of patients used in this study were listed as Table 1.

Table 1. The clinical characteristics of patients used in this study

GEO data set	GEO accession	Disease type	Dementia status	Age	Sex	Country
GSE188847	GSM5690952	COVID-19	No dementia	46	Male	United States
	GSM5690953	COVID-19	No dementia	57	Female	United States
	GSM5690954	COVID-19	No dementia	58	Female	United States
	GSM5690955	COVID-19	No dementia	62	Male	United States
	GSM5690956	COVID-19	No dementia	62	Male	United States
	GSM5690957	COVID-19	No dementia	64	Male	United States
	GSM5690958	COVID-19	No dementia	65	Male	United States
	GSM5690959	COVID-19	No dementia	72	Female	United States
	GSM5690960	COVID-19	No dementia	75	Female	United States
	GSM5690961	COVID-19	No dementia	75	Male	United States
	GSM5690962	COVID-19	No dementia	80	Female	United States
	GSM5690963	COVID-19	No dementia	84	Male	United States
	GSM5690964	Control	No dementia	45	Male	United States
	GSM5690965	Control	No dementia	59	Female	United States
	GSM5690966	Control	No dementia	59	Female	United States
	GSM5690967	Control	No dementia	61	Male	United States
	GSM5690968	Control	No dementia	62	Male	United States
	GSM5690969	Control	No dementia	64	Male	United States
	GSM5690970	Control	No dementia	64	Male	United States
	GSM5690971	Control	No dementia	71	Female	United States
	GSM5690972	Control	No dementia	75	Female	United States
	GSM5690973	Control	No dementia	75	Male	United States
	GSM5690974	Control	No dementia	80	Female	United States
	GSM5690975	Control	No dementia	84	Male	United States
GSE164332	GSM5006430	Control	Dementia	78	Female	Italy
	GSM5006432	Control	Dementia	84	Male	Italy
	GSM5006433	Control	Dementia	85	Female	Italy
	GSM5006434	Control	Dementia	80	Male	Italy
	GSM5006435	Control	Dementia	104	Female	Italy
	GSM5006436	Control	Dementia	84	Female	Italy
	GSM5006437	COVID-19	Dementia	74	Female	Italy
	GSM5006438	COVID-19	Dementia	87	Male	Italy
	GSM5006439	COVID-19	No dementia	67	Male	Italy
	GSM5006440	COVID-19	Dementia	94	Female	Italy
	GSM5006442	COVID-19	No dementia	80	Female	Italy
	GSM5006443	COVID-19	Dementia	83	Female	Italy
	GSM5006444	COVID-19	Dementia	92	Male	Italy
	GSM5006445	COVID-19	Dementia	81	Male	Italy

2.2 RNA-seq analysis

Raw RNA-seq data were downloaded from the GEO database using the ARCHS4 tool and aligned against the GRCh38 human reference genome; the transcript counts were mapped to the gene level using human_matrix_v10 using R software (version 4.2.0).²⁰ Differentially expressed genes (DEGs) were analyzed using NetworkAnalyst 3.0.²¹ Genes with a variance percentile rank lower than 15 and count lower than 4, and unannotated genes were removed. Normalization was performed by log2-counts per million transformation. The DESeq2 algorithm was used to identify DEGs according to the criteria |log2 fold change| > 0.6 and p < 0.05.

2.3 Functional enrichment analysis and gene set enrichment analysis (GSEA)

Process enrichment analysis of DEGs was conducted with Gene Ontology (GO) Biological Processes sources using Metascape (https://metascape.org).²² Terms with a p < 0.05 were considered significantly enriched. Crucial pathways involved in COVID-19 were analyzed with the gene set database c2.cp.kegg.v7.5.1.symbols.gmt and 1000 permutations using GSEA 4.2.3 software.²³ According to the instructions, if the sample size of each phenotype was more than 7, the permutation type chose phenotype; if the sample size of each group was more than 3, the permutation type chose gene_set and the genes were ranked by the Signal2Noise. Each phenotype has less than three samples, the permutation type chose gene_set and the genes were ranked by the Diff_of_Classes. Pathways with nominal p < 0.05 and |Normalized Enrichment Score| > 1 were considered statistically significant.

2.4 Protein–protein interaction (PPI) network construction and hub gene analysis

The PPI network of DEGs was constructed using STRING v11.5 and the interaction score was set as medium confidence (0.400).²⁴ Then the network was visualized using Cytoscape v3.8.2.²⁵ The centralities of each node were calculated using CentiScaPe 2.2 plug-in, and the nodes were ranked based on degree.²⁶ Genes with the top 20 degree values were identified as hub genes.

2.5 Immune cell infiltration analysis

xCell (https://xcell.ucsf.edu/) is a webtool that performs cell type enrichment analysis of 36 immune cell types based on gene signatures using bulk gene expression data.²⁷ The gene-level read counts were normalized to transcripts per million and then used to estimate the effects of COVID-19 on the brain infiltrating immune cell subsets and abundance using xCell.

2.6 Statistical analysis

Data were expressed as the means ± standard deviation. Differences between groups were analyzed for significance by the unpaired t-test using GraphPad Prism 9 (GraphPad Software Inc.). Pearson's correlation was calculated to verify the relationship between hub genes and dementia-related biomarker genes (APP, MAPT, and SNCA) on the basis of the normalized expression values. A p-value less than 0.05 was defined as statistically significant.

3 RESULTS

3.1 COVID-19 enhanced the Th2 cell-mediated humoral immune response and reduced dementia pathogenesis in patients without dementia

We first determined the effects of COVID-19 on the gene transcription levels in the frontal cortex of the human brain from GEO GSE188847. As shown in Figure 1A, A total of 1507 differentially expressed genes were identified according to the criteria |log2 fold change| > 0.6 and p < 0.05, including 989 upregulated and 518 downregulated DEGs. Biological process enrichment analysis revealed that the 989 upregulated DEGs were enriched in immune-related processes, such as inflammatory response, cellular response to cytokine stimulus, positive regulation of immune response, cytokine production, and immune system process, and were also strongly related to the regulation of defense response, positive regulation of cell death and response to virus (Figure 1B). Meanwhile, the 518 downregulated DEGs were mainly distributed in nervous system-related processes, such as synaptic signaling, chemical synaptic transmission, neuropeptide signaling pathway, synapse organization, and synapse structure or activity, indicating that COVID-19 might affect patients' behavior and brain function.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

COVID-19 enhanced the Th2 cell-mediated humoral immune response and reduced dementia pathogenesis in patients without dementia. (A) The volcano plot of DEGs between COVID-19 patients and controls without dementia in GSE188847 data set. (B) Biological process enrichment analysis of 989 upregulated (left) and 518 downregulated (right) DEGs was performed using Metascape. (C) The genes of GSE188847 data set were ranked by the Signal2Noise, and GSEA was performed using GSEA 4.2.3. (D) Expression levels of APP, MAPT, and SNCA in COVID-19 patients and controls without dementia. (E) A PPI network of DEGs was generated by STRING v11.5 and then visualized by Cytoscape v3.8.2. The node size and color were ranked by the degree of each node. (F) Pearson's correlation analysis was performed between the 20 hub genes and 3 dementia-related biomarker genes (APP, MAPT, and SNCA) with the normalized expression values. (G) The infiltrating immune cell subsets and abundance of 36 immune cell types were calculated using xCell with gene-level TPM values. (H) The Th1/Th2 ratio in nondemented COVID-19 patients and controls. *p < 0.05, **p < 0.01, ***p < 0.001. DEG, differentially expressed gene; PPI, protein–protein interaction; Th1, T helper type 1; Th2, T helper type 2; TPM, transcripts per million.

As shown in Figure 1C, the GSEA results demonstrated that COVID-19 obviously increased the enrichment scores of immune-related pathways, such as Complement and coagulation cascades (p = 0.016), Cytokine–cytokine receptor interaction (p = 0.014), Toll-like receptor signaling pathway (p = 0.024), as well as Apoptosis (p = 0.045) and RNA degradation (p = 0.047). Conversely, patients with COVID-19 showed decreased enrichment scores of Calcium signaling pathway (p = 0.12), Alzheimer's disease (p = 0.428), and Parkinson's disease (p = 0.257) compared with patients in the control group.

Strikingly, COVID-19 significantly decreased the levels of dementia-related biomarker genes APP, MAPT, and SNCA (Figure 1D). Then, we constructed a PPI network and identified 20 hub genes from the network (Figure 1E). To further verify the involvement of COVID-19 and dementia, we performed a correlation analysis between hub genes and dementia-related biomarkers. As shown in Figure 1F, expression levels of 20 hub genes were significantly changed in patients with COVID-19. The 18 upregulated hub genes in COVID-19 negatively correlated with dementia-related biomarkers, while 2 downregulated hub genes positively correlated with dementia-related biomarkers.

As aging is an important risk factor for dementia, we divided the patients into younger and older groups based on the mean age and explored the potential effects of COVID-19 in different age groups.^28-30 GSEA results revealed that COVID-19 increased the enrichment scores of immune-related pathways and reduced the enrichment scores of Alzheimer's disease and Parkinson's disease in both younger and older groups (Supporting Information: Figure 1A,C). However, this tendency in the elder group was more obvious than that of the younger group. A decreased expression levels of APP, MAPT, and SNCA were observed in patients with COVID-19 in both younger and older groups (Supporting Information: Figure 1B,D).

To confirm the association of COVID-19 and the immune response, we analyzed the immune cell infiltration in frontal cortex tissues. Compared with the control group, COVID-19 patients exhibited higher levels of myeloid dendritic cell activated, endothelial cell, macrophage M1, monocyte and T cell CD4 + Th2, but a lower level of common myeloid progenitor (Figure 1G). In addition, the ratio of Th1/Th2 cells was dramatically decreased from 13.58 in the control group to 2.01 in the COVID-19 group (Figure 1H). These findings indicate that the host immune system might protect the nervous system from SARS-CoV-2 infection by inhibiting the Th1 cell-mediated cellular immune response.

3.2 COVID-19 decreased the Th2 cell-mediated humoral immune response and accelerated dementia pathogenesis in patients with dementia

Next, we assessed the influence of COVID-19 in patients with dementia. We extracted 803 DEGs, including 333 upregulated and 470 downregulated DEGs (Figure 2A). Functional enrichment analysis showed that the 333 upregulated DEGs were closely associated with cytoplasmic translation, ribosomal small subunit biogenesis, mRNA processing, cellular component biogenesis, and regulation of synaptic transmission, dopaminergic (Figure 2B). The 470 downregulated DEGs were markedly correlated with cellular response to cytokine stimulus, response to wounding, inflammatory response, and cellular homeostasis.

As shown in Figure 2C, GSEA confirmed that COVID-19 was positively correlated with the pathways Alzheimer's disease (p = 0.004), Parkinson's disease (p < 0.001), and Ribosome (p < 0.001), but was negatively interrelated with the pathways Complement and coagulation cascades (p < 0.001), Cytokine–cytokine receptor interaction (p < 0.001), Toll-like receptor signaling pathway (p < 0.001), Intestinal immune network for IgA production (p < 0.001), Calcium signaling pathway (p < 0.001), and Peroxisome (p = 0.018).

As shown in Figure 2D, COVID-19 slightly increased levels of APP, MAPT, and SNCA. From the PPI network, we identified 20 hub genes (Figure 2E). Furthermore, higher levels of the 20 hub genes were observed in demented COVID-19 patients than in demented controls (Figure 2F). All 20 hub genes were positively associated with dementia-related biomarker genes.

According to the mean age, patients in GSE164332 data set were divided into younger and older groups. Compared with controls, the enrichment scores of Alzheimer's disease and Parkinson's disease pathways were increased, while the immune-related pathways were downregulated in patients with COVID-19 in both younger and older groups (Supporting Information: Figure 2A,C. Meanwhile, the enrichment scores of immune-related pathways were decreased in patients with COVID-19 in both groups, except Cytokine–cytokine receptor interaction pathway in the younger group. As shown in Supporting Information: Figure 2B,D, patients with COVID-19 exhibited high expression levels of APP, MAPT, and SNCA in both groups.

Xcell analysis revealed that the infiltration level of T cell CD4 + Th1 was significantly upregulated in demented COVID-19 patients (p < 0.001, Figure 2G). In addition, patients with COVID-19 showed a sharp increase of Th1/Th2 ratio, from 0.16 to 33.32 (Figure 2H). The results suggest that COVID-19 might impair the nervous system and promote the progression of dementia-related diseases by activating Th1 cell-mediated cellular immunity.

3.3 Molecular signatures of COVID-19 patients with and without dementia

To clarify the different responses of patients with and without dementia during COVID-19, we analyzed the expression profiles of frontal cortex tissues from 6 demented and 2 nondemented patients who were infected with SARS-CoV-2. As shown in Figure 3A, 196 DEGs were obtained, including 79 upregulated and 117 downregulated DEGs. The 79 upregulated DEGs showed a significant association with mitogen-activated protein kinase (MAPK) cascade, endothelial cell migration, cellular response to acid chemical, lipoprotein metabolic process, and cellular response to hypoxia (Figure 3B). The 117 downregulated DEGs were obviously enriched in several immune-related biological processes, such as stimulatory C-type lectin receptor signaling pathway, inflammatory response, positive regulation of leukocyte cell-cell adhesion, lymphocyte-mediated immunity, and leukocyte differentiation.

The GSEA results demonstrated that Calcium signaling pathway (p < 0.001), Neuroactive ligand-receptor interaction (p < 0.001), Leishmania infection (p < 0.001), ABC transporters (p = 0.003), and Peroxisome (p = 0.026) were enriched in patients without dementia. However, the enrichment degree of Olfactory transduction (p < 0.001), Ribosome (p = 0.10), Regulation of autophagy (p = 0.060), Porphyrin and chlorophyll metabolism (p = 0.079) was significantly higher in demented patients than in nondemented patients (Figure 3C). During COVID-19, demented patients exhibited less B cell infiltration, and a higher level of T cell CD4 + Th1 than nondemented patients (Figure 3D). The ratio of Th1/Th2 was significantly increased from 1.17 in nondemented patients to 33.32 in demented patients (Figure 3E).

4 DISCUSSION

In the present study, we found that COVID-19 was strongly associated with nervous system processes in both demented and nondemented, but the effects of COVID-19 on the DEG expression profile differed between the groups. In nondemented patients, COVID-19-downregulated DEGs were related to synaptic function and neuropeptide signaling pathway. In demented patients, COVID-19-upregulated DEGs were related to synaptic function. The GSEA results also revealed an unequivocal role of COVID-19 in the progression of dementia-related diseases. COVID-19 could slightly attenuate the risk of Alzheimer's disease and Parkinson's disease in nondemented patients. Meanwhile, COVID-19 significantly increased the risk of dementia in demented patients. Although aging is a major risk factor for cognitive deficits and neurodegenerative diseases, we observed the same pattern of COVID-19 on immune response and dementia in the younger and elder groups. One study reported that COVID-19 can cause cognitive decline in young patients.³¹ This does not seem to conflict with our conclusion because even for healthy people, long-term isolation and hospitalization can also cause a noticeable impact on mental state.

Subsequently, we identified 20 hub genes and performed a correlation analysis of the expression levels of the 20 hub genes and 3 dementia-related biomarkers. APP and MAPT genes encode amyloid beta peptides and microtubule-associated protein tau, respectively. Amyloid beta accumulation and abnormal tau tangles are the pathological hallmarks of Alzheimer's disease.³² Mutations of MAPT have also been discovered as one of the main genetic causes of frontotemporal dementia.³³ SNCA gene, encoding alpha-synuclein (alphaSyn), was the first gene identified to cause Parkinson's disease.³⁴ We observed that COVID-19 significantly reduced the expression of APP, MAPT, and SNCA in patients without dementia, and their expression levels were associated with that of 20 hub genes. In patients with dementia, however, COVID-19 increased dementia-related gene expression and was positively correlated with the levels of 20 hub genes. These findings suggest that the dementia status before infection may ultimately determine the effects of COVID-19 on cognitive function.

Accumulating evidence suggests that neuroinflammation is a key player in the pathogenesis of Alzheimer's disease.³⁵ The overactive immune response and cytokine storm observed in the acute stage of SARS-CoV-2 infection are believed to contribute to neuronal damage and synaptic dysfunction.^36-38 Notably, it was recently reported that coadministration with anti-inflammatory agents, such as steroid, colchicine, and vitamin C, can reduce the incidence and improve the prognosis of COVID-19.^39-41 These findings indicate that COVID-19-induced neuroinflammation might exacerbate the evolution of neurodegeneration.

In this study, we observed that SARS-CoV-2 infection increased the infiltration levels of myeloid dendritic cell activated, endothelial cell common myeloid progenitor, macrophage M1, monocyte and T cell CD4 + Th2, and decreased the infiltration level of common myeloid progenitor endothelial cells in patients without dementia. In addition, the Th1/Th2 ratio was strikingly reduced following COVID-19. These results suggested that COVID-19 suppressed Th1 cell-mediated cellular immunity in nondemented patients. Meanwhile, GSEA results further revealed that the signaling pathways associated with complement, Toll-like receptor, and cytokine receptor were enhanced after COVID-19, suggesting that Th2 cell-mediated humoral immunity was activated. Our findings imply that in COVID-19, the immune system of nondemented patients could not produce sufficient cytotoxic T cells and Th1 type cytokines to inhibit virus replication and remove the virus from the host, which results in chronic SARS-CoV-2 infection and immune escape. This compromise between the host and virus can reduce the damage that cytokine storms inflict upon brain nerves. In contrast, an opposite result was observed in COVID-19 patients with dementia. In this situation, SARS-CoV-2 infection promoted a Th1 cell-mediated cellular response and generated large amounts of Th1-type cytokines. This reaction was helpful to clear SARS-CoV-2, but overactive immune responses may further aggravate neuronal damage and cognitive impairment in patients with dementia. This difference in the immune response between dementia and non-dementia may partially explain the distinct effects of COVID-19 on cognition. Moreover, these findings suggest that appropriate anti-inflammatory drugs should be coadministered for COVID-19 patients with dementia, but not recommended for COVID-19 patients without dementia.

Finally, we explored the possible mechanisms responsible for the distinct immune response. GO biological processes analysis indicated enrichment in the MAPK cascade, endothelial cell migration, cellular response to hypoxia, inflammatory response, and lymphocyte-mediated immunity. MAPK signaling pathway is a highly conserved signal transduction pathway that has a major role in regulating innate immunity during pathogen infection.⁴² Recently, MAPK hyper-activation has been observed in the lung, heart, and platelets of COVID-19 patients.^43-45 Hypoxia is a major pathological feature of COVID-19. Studies have indicated that hypoxia can upregulate the expression of COVID-19 receptor ACE2 and increase the production of pro-inflammatory cytokines.^46-48 In addition, our data suggest a potential role for peroxisome and autophagy during COVID-19, which are important regulators of amyloid beta clearance and tau hyperphosphorylation.^{49, 50}

There were several limitations to this study. First, although the participants in this study contained COVID-19 patients with and without dementia as well as age-matched controls, the sample size is still small. Second, our findings were based on the whole-transcriptome sequencing data, direct evidence from histopathological changes, such as senile plaques, neurofibrillary tangles, and dementia severity, are lacking. Third, the human brain tissue samples were obtained through autopsy and long-term clinical follow-up data are not available, especially the cognition alteration of patients after SARS-CoV-2 infection. In the future, much needs to be done to authenticate these findings by longitudinal follow-up from a large sample size. It is also important to obtain evidence from cell and animal experiments when the experimental conditions permit.

In summary, the present study uncovered a distinct influence of COVID-19 on cognitive function and immune response between demented and nondemented patients. Furthermore, our findings identified many potential biomarkers and molecular mechanisms responsible for the impacts of COVID-19 on dementia. Overall, our work may serve as an important clinical reference for COVID-19 treatment in elderly patients.

AUTHOR CONTRIBUTIONS

Xiaojiang Hao and Hui Song conceptualized the study. Jue Yang and Hui Song contributed to data collection, performed analysis and interpretation, and wrote the paper draft. Xiaojiang Hao revised the manuscript. All authors contributed to the article and approved the final manuscript.

ACKNOWLEDGMENTS

This study was supported by grants from the National Natural Science Foundation of China (82160813, 32060210, U1812403), the State Key Laboratory of Drug Research (SIMM2105KF-15), Guizhou Provincial Natural Science and Technology Projects (QKHZK[2021]526, QKHZK[2021]448), the Project of Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education, Guizhou Medical University (QJHKY[2020]251) and the Cultivation Project of National Natural Science Foundation of China of Guizhou Medical University (20NSP064 and 19NSP008), (Qian Ke He Ping Tai Ren Cai (2019])5106 and TianchanziJzi (2021)07.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Open Research

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Supporting Information

REFERENCES

1 World Health Organization. Coronavirus Disease (COVID-19) Pandemic. 2022. Accessed June 30, 2022. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
Google Scholar
2 CDC COVID-19 Response Team. Severe outcomes among patients with coronavirus disease 2019 (COVID-19)—United States, February 12–March 16, 2020. MMWR Morb Mortal Wkly Rep. 2020; 69(12): 343-346.
10.15585/mmwr.mm6912e2
PubMed Web of Science® Google Scholar
3Bastard P, Gervais A, Le Voyer T, et al. Autoantibodies neutralizing type I IFNs are present in ~4% of uninfected individuals over 70 years old and account for ~20% of COVID-19 deaths. Sci Immunol. 2021; 6(62):eabl4340.
10.1126/sciimmunol.abl4340
PubMed Web of Science® Google Scholar
4Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020; 382(8): 727-733.
10.1056/NEJMoa2001017
CAS PubMed Web of Science® Google Scholar
5Onder G, Rezza G, Brusaferro S. Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA. 2020 May 12; 323(18): 1775-1776. doi:10.1001/jama.2020.4683. Erratum in: JAMA. 2020;323(16):1619.
CAS PubMed Web of Science® Google Scholar
6Paniz-Mondolfi A, Bryce C, Grimes Z, et al. Central nervous system involvement by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). J Med Virol. 2020; 92(7): 699-702.
10.1002/jmv.25915
CAS PubMed Web of Science® Google Scholar
7Thakur KT, Miller EH, Glendinning MD, et al. COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital. Brain. 2021; 144(9): 2696-2708.
10.1093/brain/awab148
PubMed Web of Science® Google Scholar
8Meinhardt J, Radke J, Dittmayer C, et al. Olfactory transmucosal SARS-CoV-2 invasion as a port of central nervous system entry in individuals with COVID-19. Nat Neurosci. 2021; 24(2): 168-175.
10.1038/s41593-020-00758-5
CAS PubMed Web of Science® Google Scholar
9Bhaskaran K, Rentsch CT, Hickman G, et al. Overall and cause-specific hospitalisation and death after COVID-19 hospitalisation in England: a cohort study using linked primary care, secondary care, and death registration data in the OpenSAFELY platform. PLoS Med. 2022; 19(1):e1003871.
10.1371/journal.pmed.1003871
CAS PubMed Web of Science® Google Scholar
10Williamson EJ, Walker AJ, Bhaskaran K, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020; 584(7821): 430-436.
10.1038/s41586-020-2521-4
CAS PubMed Web of Science® Google Scholar
11Hippisley-Cox J, Coupland CA, Mehta N, et al. Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study. BMJ. 2021; 374: n2244.
10.1136/bmj.n2244
PubMed Google Scholar
12 Alzheimer's Association. 2022 Alzheimer's disease facts and figures. Alzheimers Dement. 2022; 18(4): 700-789.
10.1002/alz.12638
PubMed Web of Science® Google Scholar
13 World Health Organization. Dementia. 2021. Accessed January 27, 2021. https://www.who.int/news-room/facts-in-pictures/detail/dementia
Google Scholar
14 Alzheimer's Association. 2020 Alzheimer's disease facts and figures. Alzheimers Dement. 2020; 16(3): 391-460.
10.1002/alz.12068
Web of Science® Google Scholar
15July J, Pranata R. Prevalence of dementia and its impact on mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis. Geriatr Gerontol Int. 2021; 21(2): 172-177.
10.1111/ggi.14107
PubMed Web of Science® Google Scholar
16Liu N, Sun J, Wang X, Zhao M, Huang Q, Li H. The impact of dementia on the clinical outcome of COVID-19: a systematic review and meta-analysis. J Alzheimers Dis. 2020; 78(4): 1775-1782.
10.3233/JAD-201016
CAS PubMed Web of Science® Google Scholar
17Soldevila L, Prat N, Mas MÀ, et al. The interplay between infection risk factors of SARS-CoV-2 and mortality: a cross-sectional study from a cohort of long-term care nursing home residents. BMC Geriatr. 2022; 22(1): 123.
10.1186/s12877-022-02779-0
CAS PubMed Web of Science® Google Scholar
18Mavrikaki M, Lee JD, Solomon IH, Slack FJ. Severe COVID-19 induces molecular signatures of aging in the human brain. medRxiv [Preprint]. 2021. doi:10.1101/2021.11.24.21266779
Google Scholar
19Gagliardi S, Poloni ET, Pandini C, et al. Detection of SARS-CoV-2 genome and whole transcriptome sequencing in frontal cortex of COVID-19 patients. Brain Behav Immun. 2021; 97: 13-21.
10.1016/j.bbi.2021.05.012
CAS PubMed Web of Science® Google Scholar
20Lachmann A, Torre D, Keenan AB, et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun. 2018; 9(1): 1366.
10.1038/s41467-018-03751-6
PubMed Web of Science® Google Scholar
21Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019; 47(W1): W234-W241.
10.1093/nar/gkz240
CAS PubMed Web of Science® Google Scholar
22Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019; 10(1): 1523.
10.1038/s41467-019-09234-6
PubMed Web of Science® Google Scholar
23Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005; 102(43): 15545-15550.
10.1073/pnas.0506580102
CAS PubMed Web of Science® Google Scholar
24Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021; 49(D1): D605-D612.
10.1093/nar/gkaa1074
CAS PubMed Web of Science® Google Scholar
25Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13(11): 2498-2504.
10.1101/gr.1239303
CAS PubMed Web of Science® Google Scholar
26Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009; 25(21): 2857-2859.
10.1093/bioinformatics/btp517
CAS PubMed Web of Science® Google Scholar
27Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017; 18(1): 220.
10.1186/s13059-017-1349-1
PubMed Web of Science® Google Scholar
28Mecocci P, Boccardi V. The impact of aging in dementia: it is time to refocus attention on the main risk factor of dementia. Ageing Res Rev. 2021; 65:101210.
10.1016/j.arr.2020.101210
CAS PubMed Web of Science® Google Scholar
29Wahl D, Solon-Biet SM, Cogger VC, et al. Aging, lifestyle and dementia. Neurobiol Dis. 2019; 130:104481.
10.1016/j.nbd.2019.104481
PubMed Web of Science® Google Scholar
30Sengoku R. Aging and Alzheimer's disease pathology. Neuropathology. 2020; 40(1): 22-29.
10.1111/neup.12626
PubMed Web of Science® Google Scholar
31Almeria M, Cejudo JC, Sotoca J, Deus J, Krupinski J. Cognitive profile following COVID-19 infection: clinical predictors leading to neuropsychological impairment. Brain Behav Immun Health. 2020; 9:100163.
10.1016/j.bbih.2020.100163
CAS PubMed Google Scholar
32Trejo-Lopez JA, Yachnis AT, Prokop S. Neuropathology of Alzheimer's disease. Neurotherapeutics. 2022; 19(1): 173-185.
10.1007/s13311-021-01146-y
CAS PubMed Web of Science® Google Scholar
33Greaves CV, Rohrer JD. An update on genetic frontotemporal dementia. J Neurol. 2019; 266(8): 2075-2086.
10.1007/s00415-019-09363-4
PubMed Web of Science® Google Scholar
34Du XY, Xie XX, Liu RT. The role of α-synuclein oligomers in Parkinson's disease. Int J Mol Sci. 2020; 21(22): 8645.
10.3390/ijms21228645
CAS PubMed Web of Science® Google Scholar
35Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021; 17(3): 157-172.
10.1038/s41582-020-00435-y
PubMed Web of Science® Google Scholar
36Amruta N, Chastain WH, Paz M, et al. SARS-CoV-2 mediated neuroinflammation and the impact of COVID-19 in neurological disorders. Cytokine Growth Factor Rev. 2021; 58: 1-15.
10.1016/j.cytogfr.2021.02.002
CAS PubMed Web of Science® Google Scholar
37Kempuraj D, Selvakumar GP, Ahmed ME, et al. COVID-19, mast cells, cytokine storm, psychological stress, and neuroinflammation. Neuroscientist. 2020; 26(5-6): 402-414.
10.1177/1073858420941476
CAS PubMed Web of Science® Google Scholar
38Tang SW, Helmeste D, Leonard B. Inflammatory neuropsychiatric disorders and COVID-19 neuroinflammation. Acta Neuropsychiatr. 2021; 33(4): 165-177.
10.1017/neu.2021.13
PubMed Web of Science® Google Scholar
39Pilotto A, Odolini S, Masciocchi S, et al. Steroid-responsive encephalitis in coronavirus disease 2019. Ann Neurol. 2020; 88(2): 423-427.
10.1002/ana.25783
CAS PubMed Web of Science® Google Scholar
40Golpour M, Mousavi T, Alimohammadi M, et al. The effectiveness of Colchicine as an anti-inflammatory drug in the treatment of coronavirus disease 2019: meta-analysis. Int J Immunopathol Pharmacol. 2021; 35:20587384211031763.
10.1177/20587384211031763
Web of Science® Google Scholar
41Holford P, Carr AC, Jovic TH, et al. Vitamin C—an adjunctive therapy for respiratory infection, sepsis and COVID-19. Nutrients. 2020; 12(12): 3760.
10.3390/nu12123760
CAS PubMed Web of Science® Google Scholar
42Arthur JS, Ley SC. Mitogen-activated protein kinases in innate immunity. Nat Rev Immunol. 2013; 13(9): 679-692.
10.1038/nri3495
CAS PubMed Web of Science® Google Scholar
43Hemmat N, Asadzadeh Z, Ahangar NK, et al. The roles of signaling pathways in SARS-CoV-2 infection; lessons learned from SARS-CoV and MERS-CoV. Arch Virol. 2021; 166(3): 675-696.
10.1007/s00705-021-04958-7
CAS PubMed Web of Science® Google Scholar
44Grimes JM, Grimes KV. p38 MAPK inhibition: a promising therapeutic approach for COVID-19. J Mol Cell Cardiol. 2020; 144: 63-65.
10.1016/j.yjmcc.2020.05.007
CAS PubMed Web of Science® Google Scholar
45Zhang S, Liu Y, Wang X, et al. SARS-CoV-2 binds platelet ACE2 to enhance thrombosis in COVID-19. J Hematol Oncol. 2020; 13(1): 120.
10.1186/s13045-020-00954-7
CAS PubMed Web of Science® Google Scholar
46Khaddaj-Mallat R, Aldib N, Bernard M, et al. SARS-CoV-2 deregulates the vascular and immune functions of brain pericytes via Spike protein. Neurobiol Dis. 2021; 161:105561.
10.1016/j.nbd.2021.105561
CAS PubMed Web of Science® Google Scholar
47Imperio GE, Lye P, Mughis H, et al. Hypoxia alters the expression of ACE2 and TMPRSS2 SARS-CoV-2 cell entry mediators in hCMEC/D3 brain endothelial cells. Microvasc Res. 2021; 138:104232.
10.1016/j.mvr.2021.104232
CAS PubMed Web of Science® Google Scholar
48Tian M, Liu W, Li X, et al. HIF-1α promotes SARS-CoV-2 infection and aggravates inflammatory responses to COVID-19. Signal Transduct Target Ther. 2021; 6(1): 308.
10.1038/s41392-021-00726-w
CAS PubMed Web of Science® Google Scholar
49Wani A, Al Rihani SB, Sharma A, et al. Crocetin promotes clearance of amyloid-β by inducing autophagy via the STK11/LKB1-mediated AMPK pathway. Autophagy. 2021; 17(11): 3813-3832.
10.1080/15548627.2021.1872187
CAS PubMed Web of Science® Google Scholar
50Moosecker S, Gomes P, Dioli C, Yu S, Sotiropoulos I, Almeida OFX. Activated PPARγ abrogates misprocessing of amyloid precursor protein, tau missorting and synaptotoxicity. Front Cell Neurosci. 2019; 13: 239.
10.3389/fncel.2019.00239
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume95, Issue1

January 2023

e28177

Filename	Description
jmv28177-sup-0001-Supplementary_Figure_1.tif3.7 MB	Supplementary Figure 1.
jmv28177-sup-0002-Supplementary_Figure_2.tif3.5 MB	Supplementary Figure 2.

Whole-transcriptome sequencing data reveals a disparate cognitive and immune signature in COVID-19 patients with and without dementia

Abstract

1 INTRODUCTION