Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

doi:10.21203/rs.3.rs-952508/v1

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Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

https://doi.org/10.21203/rs.3.rs-952508/v1

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More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

Biomedical Engineering

bioinformatical analysis

Alzheimer’s disease

type 2 diabetes mellitus

actin filament

autophagy

Alzheimer’s disease(AD) is the most common cause of dementia[1].The patients with AD are seted to increase triple over the next 30 years [2]. The etiology and pathogenesis of AD was unclear.Mounting evidence suggests the association of AD with T2DM. The World Health Organization classify diabetes mellitus（DM） into four groups: Type 1 DM (T1DM), Type 2 DM (T2DM), Other Types, and Gestational Diabetes. T2DM accounts for most of all diabetes cases[3]. The AD patients manifests abnormal clinical ,which are similar to diabetes, so AD is sometimes called “Type 3 diabetes”[4]. Similarly,the etiology and pathogenesis of DM are extremely complicated and unclear.Also, both of diseases can not be cured. Increasing evidence supports that diabetes is associated with the pathogenesis of AD[5].

The relationship between these two diseases however remains unclear. Studies have revealed that T2DM may be the development risk of AD. However, the relationship and mechanism between the two is unclear. Perturbed cerebral glucose metabolism may be the key factor in the pathogenesis of AD.[5] [6]Some factors such as hyperglycemia, peripheral hyperinsulinemia, lipid metabolism processes , and chronic inflammation with T2DM can be correlated with this key factor of AD[7]. However, the evidence is not always clear cut. There is currently no direct evidence to prove the relationship between the two diseases . In this study, we screened for potential biomarkers and therapeutic targets in T2DM-related AD based on bioinformatics analysis. Our goal is to advance our understanding the correlation of AD and T2DM.

Data preprocessing and identifification of DEGs

We retrieved profile datasets of AD and T2DM from the NCBI Gene Expression Omnibus (GEO) database [8].We retrieve the keywords "Alzheimer's Disease" and "Type 2 diabetes mellitus". These datasets were screened based on some conditions which were as follows:1.human being 2.RNA expression profiles based on PBMC. Finaly,we selected GSE95849(T2DM including patients and the normal control ) and GSE97760(AD including patients,and the normal control).According to the GSE95849 analysis of UMAP and PCA ,we eliminated 1 patient. AD and T2DM differentially expressed genes (DEGs) were analyzed using the GEO2R[9]. The DEGs was Screened with the cut-off value p < 0.05 and │Log2fold change (FC)│ > 1.5. We compared two datasets DEGs and visualized the ggplot2 package (version 3.3.3). (Figure 1)

Functional and Pathway annotation of significant DEGs

Following identification of DEGs, gene ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs by useing ggplot2 package (version 1.30.0) [10]. Functional and Pathway were Screened of p-value <0.05.(Figure 2)

Constructing protein-protein interaction (PPI) network and revealing hub genes

The PPI network was generated with the Search Tool for Retrieval of Interacting Genes (STRING) (https://string-db.org/) [11]with a confidence score >0.40. We downloaded the DEGs interaction network from the STRING database.

We imported the database into Cytoscape[12]. Finally, we played with the APP “CytoHubba”[13] which can identify hub nodes by MCC analysis method.

Identification of 255 communal DEGs

In total, when comparing the samples of patients and normal controls, we identified 1720 genes(1619 upregulated and 101 downregulated genes) in GSE95849 (Figure 1A)and 5476 genes (2891 upregulated and 2585 downregulated genes) in GSE97760(Figure 1B).We were identify 255 communal DEGs by comparing with the DEGs of the two series(Figure 1C).

Functional and Pathway annotation of significant DEGs

Screening of Functional and Pathway annotation of significant communal DEGs between T2DM and Alzheimer's Disease. We enriched 34 GOs which subsumes 34 GO:BP(Table 1). These results suggested that negative regulation of actin filament depolymerization and regulation of actin filament depolymerization could be correlate AD with T2DM.(Figure 2)

Table 1

Functional and Pathway annotation of significant DEGs
ONTOLOGY	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
BP	GO:0030835	negative regulation of actin filament depolymerization	7/222	41/18670	4.88e-07	0.002	0.002
BP	GO:0032271	regulation of protein polymerization	13/222	218/18670	2.19e-06	0.003	0.002
BP	GO:0030834	regulation of actin filament depolymerization	7/222	51/18670	2.27e-06	0.003	0.002
BP	GO:0030042	actin filament depolymerization	7/222	54/18670	3.37e-06	0.003	0.003
BP	GO:0051693	actin filament capping	6/222	38/18670	5.31e-06	0.004	0.003

Hub nodes in the PPI network focused on autophagy

We used STRING to perform the PPI network from DEGs(Figure 3A). The results showed the correlations for the DEGs. We imported this results to the Cytoscape and CytoHubba plugin(Figure 3B). Finally, we got 30 hub genes (Table 2)and selected top 5 score hub genes. These 5 hub genes screening of functional and pathway annotation like significan DEGs(Figure 4). According to this results and hub genes score, two diseases might correlate with negative regulation of autophagy.

Table 2

Top 30 genes in network ranked by MCC method.
Rank	Name	Score
1	TP53	82
2	PINK1	35
3	SNCA	32
4	MAP1LC3A	31
5	PIK3CA	29
6	ATXN3	27
7	MDM2	22
8	RPS6KB1	15
9	ATRX	14
10	AGFG1	12
10	EEA1	12
12	RAB11A	11
12	GSN	11
14	TNS1	10
15	ADRB2	9
15	STX7	9
15	IL1R1	9
15	RAD51C	9
19	TMOD1	8
19	TMOD2	8
19	TMOD3	8
22	GNAI3	6
22	RAD23B	6
22	CCR2	6
22	CDC34	6
26	DMTN	5
26	NLRC4	5
26	NDUFB3	5
29	DCTN1	4
29	SPTB	4

In this study, identified the DEGs and the communal DEGs. The co-DEGs may be the key factor in the two diseases. For in-depth analysis and a higher likelihood of the data, we screening of functional an pathway annotation of significant DEGs. Actin filament may be play a critical role in the AD and T2DM.The actin cytoskeleton facilitates participate in the insulin signaling pathway[14][15]. Alzheimer’s disease can be leaded by the actin filament disassembly in dendritic spines[16]. Numerous studies suggested that a functional role for the actin filament in the AD and T2DM. Moreover, the peripheral insulin resistance impact brain insulin resistance, which are the key factor influencing of AD[17]. It is possible that obese patients with aging and aging-related neurodegenerative diseases have a higher level of insulin resistance [18].

In depth analysis of the DEGs with the two diseases, which screened 30 hub genes. A higher score indicates a higher likelihood of the prediction being true. The hub genes were subjected to GO and KEGG pathway enrichment analysis. We found that the majority of the results of enrichment analysis was about autophagy. Many studies show that autophagy deficits leaded to the early stage of AD and the drugs related to autophagy may be the new target for AD.[19] There is associated with improvements in insulin resistance, which have been demonstrated by the autophagy-lysosome pathway[20]. Metformin, as the first choice in T2DM, is may have a protective effect on T2DM patients by autophagy pathway[21]. And metformin reduces the symptoms in mouse models of AD[22]. This means that metformin have the therapeutic potential for AD[22].

The top 5 hub genes(TP53,PINK1,SNCA,MAP1LC3A,PIK3CA) were determined on PPI basis. These genes are associated with autophagy-related genes. TP53 regulates autophagy of cytoplasmic and nuclear[23]. Previous studies have shown that TP53 plays an important role in autophagy. However, much of the existing published literature consists of studies were associated with tumors. PINK1 suggests mitophagy to mediate autophagy [24]. Many studies showed that SNCA content effect in causing Parkinson disease (PD)[25]. And some factors may induce autophagy, regulation of SNCA expression levels is linked to the development of neuropathology. MAP1LC3A was one of the biomarker for the cellular autophagy. We can testing of MAP1LC3A expression indicate autophagy. PIK3CA/AKT signaling pathway is a upstream effector of the mTOR pathway, which mainly regulate autophagy[26]. Much of studies suggested that these genes and T2DM-rerelated AD were linked to the autophagy. One of the genes may be the key factor of the two diseases or demonstrate the gene-disease associations. These hub genes may be the potential biomarkers targets for the two diseases.

There are some limitations of this study. First, the sample number in this study was small. Second, we need a verification test to confirm the reliably of the DEGs and the hub nodes.Additionally, more in vitro and in vivo verification tests are needed to confirm the meanings and functions

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Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

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Version 1

Abstract

Figures

Introduction

Materials And Methods

Data preprocessing and identifification of DEGs

Functional and Pathway annotation of significant DEGs

Constructing protein-protein interaction (PPI) network and revealing hub genes

Results

Identification of 255 communal DEGs

Functional and Pathway annotation of significant DEGs

Hub nodes in the PPI network focused on autophagy

Discussion

References

Status:

Version 1