Bioinformatic Analysis of Genetic Factors from Human Blood Samples and Postmortem Brains in Parkinson’s Disease

Yao, Longping; Lin, Kai; Zheng, Zijian; Koc, Sumeyye; Zhang, Shizhong; Lu, Guohui; Skutella, Thomas

doi:https://doi.org/10.1155/2022/9235358

Oxidative Medicine and Cellular Longevity

On this page

Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Ethical Approval Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 9235358 | https://doi.org/10.1155/2022/9235358

Bioinformatic Analysis of Genetic Factors from Human Blood Samples and Postmortem Brains in Parkinson’s Disease

Longping Yao,¹Kai Lin,²Zijian Zheng,³Sumeyye Koc,⁴Shizhong Zhang,⁵Guohui Lu,³and Thomas Skutella¹

Academic Editor: Sachchida Nand Rai

Received19 Oct 2022

Revised08 Dec 2022

Accepted09 Dec 2022

Published24 Dec 2022

Abstract

Parkinson’s disease (PD) is one of the most prevalent neurodegenerative disorders characterized by motor and nonmotor symptoms due to the selective loss of midbrain dopaminergic neurons. Pharmacological and surgical interventions have not been possible to cure PD; however, the cause of neurodegeneration remains unclear. Here, we performed and tested a multitiered bioinformatic analysis using the GEO and Proteinexchange database to investigate the gene expression involved in the pathogenesis of PD. Then we further validated individual differences in gene expression in whole blood samples that we collected in the clinic. We also made an interaction analysis and prediction for these genetic factors. There were in all 1045 genes expressing differently in PD compared with the healthy control group. Protein-protein interaction (PPI) networks showed 10 top hub genes: ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1. We validated the ten hub gene expression in clinical PD patients and showed the expression of MDH2 was significantly different compared with healthy control. Besides, we also identified the expression of G6PD, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were all significantly increased, and levels of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were all significantly decreased in PD. Among them, to our knowledge, we presently have the most recent and conclusive evidence that GRID2, RIPK2, CUL4B, E2F2, and ABCA7 are possible PD indicators. We confirmed several genetic factors which may be involved in the pathogenesis of PD. They could be promising markers for discriminating the PD and potential factors that may affect PD development.

1. Introduction

Parkinson’s disease (PD) is an age-related progressive neurodegenerative disorder caused by selective loss of midbrain dopaminergic (DA) neurons in the substantia nigra pars compacta (SNpc) [1–5]. It has been a worldwide public health problem characterized by motor and nonmotor symptoms [6, 7]. Lewy bodies, protein aggregates containing α-synuclein, are often deposited in several brain areas of people with PD [8]. Motor symptoms, primarily dependent on dopaminergic nigrostriatal denervation, gradually manifest as DA neuron survival decreases [1]. People with PD also have sleep issues, exhaustion, changed mood, cognitive problems, autonomic dysfunction, and pain as the illness progresses and neurodegeneration worsens [9]. These symptoms result from alterations occurring at various levels of the brain. The primary pathogenic alteration is the gradual degradation of neurons in the substantia nigra pars compacta, one of the basal ganglia’s nuclei [10]. These neurons are involved in the transmission of dopamine to the striatum and another basal ganglia nucleus. The neural circuits, which comprise the basal ganglia and motor cortical regions, become dysfunctional due to the degeneration of these neurons [10]. In the end, these modifications to behavior at the level of an individual lead to movement irregularities, the main symptoms of PD, which impact the quality of life of the person. Currently, pharmacological and surgical treatment cannot to prevent or cure this disease [11]; however, understanding of neurodegeneration remains poor.

To date, some genetic biomarkers have been reported to be associated with the pathogenesis of PD [12]; such as α-synuclein, glucocerebrosidase, leucine-rich repeat kinase 2, and synaptojanin 1, can be useful to diagnose or assess the risk of PD [10]. However, these factors can only partly explain the pathogenesis of PD in some patients. Thus, further studies on the genetic architecture of PD are urgently needed. Transcriptomics and proteomics studies have been widely used to explore neurogenesis in PD [13, 14]. However, single-chip testing cannot fully reflect the complete genetic spectrum of PD because of the small sample size and differences in sample populations. In this study, we aimed to discover some potential markers for discriminating the PD and potential factors affecting PD development. Here, we combined the datasets from Gene Expression Omnibus (GEO) and Proteinexchange and validated the genes associated with PD. We analyzed the variable genes with weighted gene coexpression network analysis (WGCNA), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses (GOFEA, KEGGFEA), Gene Ontology (GO), and STRING protein-interacting network and confirmed hub interacting proteins/genes expression, which showed the metabolic pathways were tightly related in the pathogenic and pathogenesis. Further, we also verified that the expression of GRID2, PADI4, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were both significantly increased, and levels of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were both significantly decreased in PD.

2. Materials and Methods

2.1. Data Acquisition and Processing

Datasets were employed from Gene Expression Omnibus (GEO) public database (https://www.ncbi.nlm.nih.gov/gds) and Proteomexchange public database (http://www.proteomexchange.org/) for the keywords “Parkinson’s disease” and “PD.” The R software has been used for sample expression matrix acquisition background correction, data downgrade extraction, normalization, log₂ transformation, and probe reannotation. R packages that were applied in this study were RMA, devtools, AnnoProbe, limma, Biobase, affyPLM, and GEOquery. If a single gene in the chip corresponded to numerous probes, the expression level of the gene was computed using the average value of these multiple probes. Using Fisher’s combined probability test, values were pooled, then adjusted for multiple comparison correction using the false discovery rate (FDR). The significance level was established at . The study workflow diagram of analysis for transcriptomic and proteomics was concluded, and the bioinformatics analysis for differential expressed genes and case–control study flow diagram was shown in Figure 1.

Mass spectrometry is a very central analytical technique for protein research and the study of biomolecules in general [15, 16]. It can be used to identify, characterize, and quantify proteins at ever-increasing sensitivity and ever more complex samples [15]. The sequence of amino acids of proteins provides contact between proteins and their coding genes via the genetic code, and, in principle, a link between cell physiology and genetics. The discovery of proteins opens a glimpse into intricate biological regulatory networks [15]. The mass spectrometry data was analyzed by Xcalibur 2.2 data processing software (Thermo Fisher Scientific Inc.). The raw data require conversion from raw format (.raw) into text format (.txt). This phase could take a few minutes depending on the quantity of files that need to be converted and the computer’s capacity to handle big volumes of data.

2.2. WGCNA and Hub Genes Selection

WGCNA is a commonly used data mining method that uses pairwise correlations between variables to analyze biological networks [17]. While it can be used to analyze various high-dimensional data sets, it is most commonly utilized in genomics. It allows researchers to define modules (clusters), intramodular hubs, and network nodes based on module membership, examine coexpression module interactions and compare network topologies (differential network analysis). Correlation networks make it easier to apply network-based gene screening approaches to find potential biomarkers or therapeutic targets. The weighted correlation network analysis was carried out using the R package, WGCNA. We estimated the dissimilarity of the modules, determined a cut line for the module dendrogram, and merged some modules to examine the modules further. We also merged the modules with a distance of less than 0.35, yielding 5 coexpression modules in the end.

2.3. Cluster Analysis

ConsensusClusterPlus [18] was used to perform cluster analysis, including agglomerative pam clustering with 1-Pearson correlation distances and resampling 80 percent of the samples for 10 repetitions. The empirical cumulative distribution function plot was used to find the appropriate number of clusters.

2.4. Functional Enrichment Analysis

We used DAVID (https://david.ncifcrf.gov/home.jsp) to run GO and KEGG functional enrichment studies to find the most highly enriched pathways, revealing the module’s potential biological importance. The values were calculated using Fisher’s exact probability approach to obtain statistically significant gene sets with meaningful functional annotations and signaling pathways. The significance level was chosen at .

2.5. Protein-Protein Interaction Network and Hub Genes Selection in the Key Module

The STRING 11.5 database (https://string-db.org/cgi/input?sessionId=boTaerIW4Ew9&input_page_show_search=on) was used to map the protein-protein interaction (PPI) network and predict the interactions of all of the proteins discovered in the study. The activity of PPI is a primary focus of cellular biology research and serves as a prerequisite for system biology [19]. Proteins interact with other proteins inside the cell to fulfill their functions, and information generated by a PPI network enhances the perception of the protein’s function [20]. To create a network map of PPI, we used the STRING database’s data analysis mode and an integrated confidence value of 0.4. The STRING platform was used to calculate the confidence score, which is a medium confidence score. The acquired PPI are evaluated using the software Cytoscape (https://cytoscape.org/) for a better visual depiction of the network and to identify hub genes [21].

2.6. Reverse Transcription-Quantitative Real-Time Polymerase Chain Reaction

Clinical whole blood samples with PD patients and healthy control were collected. Total RNA was extracted according to the manufacturer’s instructions using TRIzol reagent (15596018; Invitrogen, Carlsbad, CA, USA). RNA was reverse transcribed to complementary DNA (cDNA) with a random primer (Sangon Biotech, Shanghai, China) using a Reverse Transcription Kit (RR047A; Takara, Dalian, China) for the quantification of messenger RNA (mRNA) of protein-encoding genes and the mRNA levels were determined using reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR). The internal control was a human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) primer pair. The Ct technique ∆∆C_t method was used to calculate and normalize relative gene expressions [22, 23]. All the sequences of the primers used are listed in Table 1.

2.7. Case Study Design

The ethics committee of Zhujiang Hospital of Southern Medical University approved this study. After receiving informed consent, a total of 40 PD patients from Zhujiang Hospital of Southern Medical University and 21 healthy controls who had not been diagnosed with a central nervous system disease in the past. The following topics were not included: (1) patients with Parkinson’s disease brought on by cardiovascular diseases, cerebrovascular illness, encephalopathy, trauma, tumors, or medications; (2) patients with various degenerative diseases, such as corticobasal degeneration, Huntington’s disease, dementia with Lewy bodies, and Alzheimer disease (AD); (3) people with other central nervous system illnesses, such as cerebrovascular disease, intracranial infection, other central nervous systemic autoimmune diseases, demyelinating disease, trauma, malignancy, toxic diseases, and metabolic diseases; (4) patients with somatic impairments, such as aphasia, severe dementia or consciousness disturbance, cancer, renal failure, hepatic failure, cardiopathy, and any other acute or chronic incapacitating or life-threatening disease/state; and (5) patients who declined to participate in the research.

The 40 peripheral blood samples of PD patients (male: 25 cases and female: 15 cases) and 21 healthy controls (male: 14 cases and female: 7 cases) were collected by the experienced nurse in the hospital. The information on sex, age, onset time, and complications was recorded. Within two hours after blood collection, blood samples were produced by centrifuging at 1200 g for 10 min at 20°C in a mini-41 C centrifuge (Heima Medical Apparatus Company, Zhuhai, China). RNA total was isolated. Based on our bioinformatic analyses, the genes whose and hub genes were selected for QT-qPCR validation.

2.8. Statistical Analysis

The results were provided as the mean SD of three separate studies. A two-tailed Student’s -test was used in the statistical analysis. A statistically significant difference was defined as one with .

3. Results

3.1. Consensus Clustering Provides Quantitative Evidence for Determining the Number and Membership of Possible Clusters in the Datasets

The GEO database has 14 PD-related microarray transcriptome datasets, and the Proteinexchange database has 45 PD-related mass spectrometry (MS) proteomics datasets. This study processed the datasets with human whole blood samples and post-mortem substantia nigra pars compacta (SNpc). Besides, each dataset group should have at least 3 or more duplicates; what is more, a healthy control group should be included in the inclusive dataset. Based on the above inclusion criteria, 2 GEO datasets and 2 Proteinexchange datasets were finally applied in this study (Table 2). 1045 differentially expressed genes (DEGs) were identified with the threshold of . These DEGs’ differential expression profiling and distribution were displayed (Figure 2(a)). We used the ConsensusClusterPlus package to perform cluster analysis, which included agglomerative pam clustering with 1-Pearson correlation distances and resampling 80 percent of the samples for 10 repetitions. The Cumulative Distribution Function (CDF) identified the best cluster number. The cluster was found to be the best stable clustering based on the CDF Delta area curve when (Figures 2(b), 2(c), and 2(d)). We used to get four Immune Subtype (IS) results (Figures 2(e) and 2(f)).

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2

Consensus clustering provides quantitative evidence for determining the number and membership of possible clusters in the datasets. (a) Volcano plots. (b) CDF curve. (c, d) Consensus clustering delta area curve (c) and column chart (d) displaying the relative change in area under the cumulative distribution function (CDF) curve for each category number compared to –1. The vertical axis depicts the relative change in area under the CDF curve, while the horizontal axis reflects the category number k. (e, f, d) Consensus sample cluster heat map. Different samples are represented by the rows and columns of the matrix. The values of the consensus matrix range from 0 (cannot be clustered together) to 1 (always clustered together) in yellow to dark red.

3.2. WGCNA Analysis the Genes which Expressed Differentially

Each of the datasets was identified by principal component analysis (PCA) (Figure 3(a)). Different databases contribute differently to the total number of proteins, with varying degrees of database overlap (Figures 3(b) and 3(c)). The 1045 DEGs were hierarchically classified into 14 categories using WGCNA analysis, respectively, black, blue, brown, cyan, green, greenyellow, grey60, lightcyan, lightgreen, lightyellow, pink, royalblue, salmon, and turquoise (Figure 3(d)). We used the network’s average connectivity (Figure 3(e)) as the horizontal axis and the scale-free topology fitting index (values in the SFT.R.Sq column in the statistical data) as the vertical axis (Figure 3(f)). In the heat map of correlation between the tree diagram of gene expression and module features, we discovered 14 modules as a result of resprung of the cluster tree (Figure 3(g)). The grouping of PD and control displayed the relationship of modules and a phenotype heat map, and the greenyellow module had the most robust inverse relationship with the PD phenotype (Figure 3(h)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 3

WGCNA analysis of the genes that expressed differentially. (a) The MetaQC software package of R was used to do the PCA analysis for each dataset. (b, c) The Venn diagrams of the four datasets. (d) 1-TOM cluster has been used to create a tree of all gene expressions. (e) Soft Threshold (Power) reflects the weight, while the vertical axis depicts the network’s average connectedness. (f) The weight is represented by Soft Threshold (Power), and the scale-free topology fitting index is shown on the vertical axis. (g) The heat maps of correlations between modules show genes and samples, with correlation coefficients and values in each cell. (h). The heat maps of correlations between modules show genes and samples, grouped into PD and control, with correlation coefficients and values in each cell.

3.3. GO and KEGG to Identify the most Highly Enriched Pathways

The DAVID was used to conduct GO and KEGG analyses to further investigate the biological processes and pathways involved (Table 3) [24]. According to GO enrichment analysis, the 1045 genes were primarily enriched in the biological progress (BP) category for the oxidation-reduction process, molecule, and metabolic drug process, cellular respiration, immune system process, regulated exocytosis, transport, the establishment of localization, and cellular respiration; the cellular component (CC) category for cytosol, vesicle, protein-containing complex, extracellular region, organelle membrane, extracellular exosome, extracellular vesicle, extracellular organelle, and endomembrane system (Figures 4(a), 4(b), and 4(c)). Many transcription factors were enriched, which were associated with PD (Table 4). The 1045 genes were mostly enriched in carbon metabolism, metabolic pathways, glycolysis/gluconeogenesis, pyruvate metabolism, synaptic vesicle cycle, endocrine, and other factor-regulated calcium reabsorption, HIF-1 signaling pathway, Huntington’s disease, PD, oxidative phosphorylation, protein processing in the endoplasmic reticulum, and Alzheimer’s disease (AD) according to KEGG pathway analysis (Figures 4(d) and 4(e)).

(a)

(b)

(c)

(d)

(e)

Figure 4

GO and KEGG to identify the most highly enriched pathways. (a) Network of enriched terms: (a) colored by cluster-ID, where nodes that share the same cluster-ID are typically close to each other; (b) colored by value, where terms containing more genes tend to have a more significant value. (b) Gene list enrichments are identified in the biological progress (BP) category, and (c) in the cellular component (CC) category. As an enrichment background, all genes in the genome were employed. Terms with a value of less than 0.01, a minimum count of three, and an enrichment factor of more than 1.5 (the enrichment factor is the ratio between the observed counts and the counts expected by chance) are gathered and classified into clusters based on membership commonalities. (d, e) KEGG biological pathways analysis of 1045 which is involved in PD.

3.4. The PPI Network Reveals Interacting Proteins and Hub Genes

STRING 11.5 was used to input the frequent DEGs, and the file generated from the analysis was reintroduced into Cytoscape for further investigation of this study, including hub gene detection [25, 26]. The PPI network investigated gene-gene/protein-protein interactions, identifying 600 pairs of interactions with a medium confidence interaction score (0.4) (Figure 5(a)). In the heat map, we chose 50 genes that showed evident alterations in PD compared with the control (Figure 5(b)). Besides, our previous research proposes a mechanism underlying neurodegeneration in chronic CNS inflammation caused by microglial activation [22, 23]. This study further confirmed that many inflammation-related genes, such as CCL16, GPR68, FPR3, IL10, SLC11A1, IL15, and LYZ, have undergone extensive changes (Figures 5(c) and 5(d)). The PPI network was utilized with a medium confidence interaction score to investigate gene-gene/protein-protein interactions with inflammation-related genes (0.4) (Figure 5(e)). Then, hub genes and essential modules were detected based on the PPIs network in Figure 5(a). We selected the top 10 hub genes of ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1 and analyzed their interactions (Figure 5(f)), and the relative expressions of the interacting proteins were shown in Figure 5(g). The expressions between NDUFS3 and UQCRC2, UQCRC1, and UQCRC2 had a significant positive correlation; meanwhile, ATP5C1 and ATP5A1 expressed a high negative correlation (Figure 5(h)). The top four hub node interacting genes’ relative expressions were ACO2, MDH2, and UQCRC1 (Figure 5(i)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Figure 5

The PPI network reveals interacting proteins. (a) The STRING web program created a PPI network for the genes in DEGs. There were 600 protein-protein interactions in total. (b) Heat map displayed the 50 genes that showed clear alterations in PD. (c) Heat map shows the expression of inflammation-related genes in PD and control groups. (d) Relative gene expression of CCL16, GPR68, FPR3, IL10, SLC11A1, IL15, and LYZ increased significantly in PD compared with the control group. Data are shown as . (e) The STRING web program created a PPI network for the inflammation-related genes. (f) The STRING web program created a PPI network for the hub genes to show the interaction between the proteins. (g) The relative hub genes levels of ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1 in PD and control group. (h) Pearson correlation coefficients among individual 10 hub genes. (i) The relative expression of top 3 hub genes ACO2, MDH2, and UQCRC1 in PD and control group.

3.5. A Case-Control Study Was Used to Validate Peripheral Blood PD Markers

RT-qPCR analyzed the peripheral blood samples of PD and healthy control to measure the gene expression. We selected the genes whose and hub genes for further validation. Totally 132 genes matched the criterion. We measured 132 genes expression in blood samples of PD patients. As a result, the levels of G6PD, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were much higher in PD than in the healthy control (Figure 6(a)). What is more, the genes expression of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were both significantly decreased in PD (Figure 6(b)). Meanwhile, there was no difference in these genes’ expression between the male and female group in both healthy control and PD patients, except for MAPK in healthy control () (Figures 6(c), 6(d), 6(e), and 6(f)). Moreover, we found that KLF9, ARF1, and ABCA7 showed a decreasing trend with the increase in disease duration (Figure 6(g)).

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Figure 6

A case-control study was used to validate peripheral blood PD markers. (a) The mRNA expression levels of G6PD, MDH2, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were studied using qRT-PCR in a total of 40 PD patients and 21 healthy controls. (b) The mRNA expression levels of MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were evaluated by qRT-PCR. (c–f) The mRNA expression of G6PD, MDH2, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, MAP 2 K1, MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were compared between healthy control and PD patients. (g) The mRNA levels of KLF9, ARF1, and ABCA7 were shown in a time-dependent matter.

3.6. The PPI Network Reveals Interaction for the Peripheral Blood PD Markers

A PPI network for the genes of G6PD, MDH2, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, MAP 2 K1, MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 was built using the STRING online tool (Figure 7(a)). The PPIs network contains a number of nodes: 40, number of edges: 91, and average node degree: 4.55. Among these factors, the ATP-binding cassette, subfamily A, member 7 (ABCA7) gene has one of the highest post-GWAS research success rates, with the discovery of both common risk variants with a direct functional consequence on ABCA7 and rare coding variants of intermediate to high penetrance providing compelling evidence of ABCA7’s involvement in AD risk [27, 28]. In this investigation, we discovered that the expression of ABCA7 in PD was dramatically reduced, implying that abnormal ABCA7 expression is linked to the pathogenic process of PD. Currently, no one has yet published this finding in PD. The STRING online tool was used to create a PPI network for ABCA7, number of nodes: 40, number of edges: 91, and average node degree: 4.55. (Figure 7(b)).

(a)

(b)

4. Discussion

PD is the second most common neurodegenerative ailment after AD, and it has become a major public health concern globally [1, 29]. Despite new proposals for numerous pathogenetic processes, the cause of neurodegeneration remains unexplained, and preventing or curing the disease is not possible at present [30]. Integrating data from various sources has previously been used to find genes with consistently high significance scores across multiple studies [31, 32].

In this study, we performed and tested a multitiered bioinformatic analysis using the GEO and Proteinexchange database to investigate the gene expression involved in the pathogenesis of PD. Because of their ease of availability compared to those from cerebrospinal fluid (SCF), molecular profiles are increasingly being employed as diagnostic biomarkers for PD as genomics and transcriptomics progress in human complex disease. An increasing variety of annotation resources is now available to prioritize and filter genomic and proteome variants. Here, we introduced the recently developed WGCNA methodology, a commonly used data mining method based on pairwise correlations between variables and is especially useful for investigating biological networks [33]. These genetic factors were also subjected to an interaction analysis and prediction.

Totally, 1045 genes were expressed differently in people with PD compared to those in the healthy control group. The following ten top hub genes were discovered in protein-protein interaction (PPI) networks: ACO2, MDH2, SDHA, ATP5A1, UQCRC2, PDHB, SUCLG1, NDUFS3, UQCRC1, and ATP5C1. Then we further validated differences in gene expression in whole blood samples collected in the clinic. MDH2, one of the key hub genes, established that the expression had significantly risen in PD compared with the control. Besides, we also identified the levels of genes G6PD, GRID2, RIPK2, CUL4B, BCL6, MRPS31, GPI, and MAP 2 K1 were both significantly increased, and levels of genes MAPK, ELAVL1, RAB14, KLF9, ARF1, ARFGAP1, ATG7, ABCA7, SFT2D2, E2F2, MAPK7, and UHRF1 were both significantly decreased in PD.

Our findings imply that these indicators, verified using clinical whole blood samples, could be used as a biomarker to distinguish between PD cases and controls. Among them, to our knowledge, we presently have the most recent and conclusive evidence that GRID2, RIPK2, CUL4B, E2F2, and ABCA7 are possible PD indicators. ABCA7 was newly identified in the early 2000s by Kaminski et al. and colleagues from human macrophages [34]. The expression of ABCA7 in PD is dramatically reduced according to our findings from the validation of peripheral blood PD samples, contrary to the expression trend already reported in AD. We found this observation fascinating, and we assume ABCA7 has excellent potential to be the key marker in PD.

Meanwhile, we also identified that many transcription factors are involved in PD pathological process. Among them, AP1 and SF1 are in the highest priority position of the enriched representative terms. Activator protein 1 (AP1) has been shown to induce neuroinflammation in PD [35]. When combined with the aberrant expression of inflammation-related genetic variables in this study, AP1 may be involved in regulating these factors. Splicing factor SF1, which is associated with the ATP-dependent formation of the spliceosome complex [36], is essential for early spliceosome assembly but not for all pre-mRNA splicing. It has also been found to operate as a transcriptional suppressor [37]. SF1 controls alternative splice site selection in mammalian cells and can control exon inclusion either positively or negatively [38]. SF1 was first discovered to be a protein necessary for pre-spliceosome formation [39]. Alternative signals may change a protein's structure or function within the cell. Thus, the wide variety of proteins that may precisely tailor cellular function to the condition of the cells’ biology is produced through alternative splicing [40]. It has been reported that SF1 is associated with aging. For example, the knockdown of human spliceosomal component SF1 in the C. elegans ortholog was sufficient to abolish lifespan extension caused by dietary restriction in C. elegans, but did not shorten wild-type lifespan. So far, no research has conclusively shown that SF1 has a role in the course of neurodegenerative diseases, including PD. In this study, we propose that SF1 is implicated in PD control and has potential research relevance.

However, the limitation of this research is that we did not conduct long-term research to see if gene expression levels can track clinical development, including the transcription factors and peripheral clinical blood PD markers that we validated in this study. We also should have investigated and confirmed the precise processes of these indicators in the PD regulation process in PD animal and cell models.

5. Conclusion

We performed the bioinformatic analysis with multimodal methods and identified 1045 genes expressing differently in PD than the healthy control group in 4 datasets. The differentially expressed genes were then further validated in clinical settings, yielding a total of 8 downregulated genes and 13 upregulated genes. They could be promising markers for discriminating the PD and potential factors that may affect PD development.

Data Availability

The datasets generated/analyzed during the current study are available.

Ethical Approval

Ethical approval was obtained from the ethics committee of Zhujiang Hospital of Southern Medical University. To take part in this study, all participants signed a written informed consent form.

Disclosure

A preprint has previously been published in: [https://http://www.researchsquare.com/article/rs-1094033/v1] [41].

Conflicts of Interest

The author(s) declare(s) that they have no conflicts of interest.

Authors’ Contributions

YLP and ST conceived and designed the experiments. YLP and LK performed the literature search, analyzed the data, and predominantly contributed to the writing of the article. YLP, SK, ZZJ, LGH, and ZSZ organized the clinical validation. Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration provided the experimental technical support. ST gave the supervision and revised the paper. Longping Yao and Kai Lin contributed equally to this work.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (81371397, 81671240, and 81560220).

References

L. Yao, J. Wu, S. Koc, and G. Lu, “Genetic imaging of neuroinflammation in Parkinson's disease: recent advancements,” Frontiers in Cell and Development Biology, vol. 9, article 655819, 2021.
View at: Publisher Site | Google Scholar
P. González-Rodríguez, E. Zampese, K. A. Stout et al., “Disruption of mitochondrial complex I induces progressive Parkinsonism,” Nature, vol. 599, no. 7886, pp. 650–656, 2021.
View at: Publisher Site | Google Scholar
S. N. Rai, P. Singh, R. Varshney et al., “Promising drug targets and associated therapeutic interventions in Parkinson's disease,” Neural Regeneration Research, vol. 16, no. 9, pp. 1730–1739, 2021.
View at: Publisher Site | Google Scholar
S. N. Rai and P. Singh, “Advancement in the modelling and therapeutics of Parkinson's disease,” Journal of Chemical Neuroanatomy, vol. 104, article 101752, 2020.
View at: Publisher Site | Google Scholar
S. N. Rai, N. Tiwari, P. Singh et al., “Exploring the paradox of COVID-19 in neurological complications with emphasis on Parkinson’s and Alzheimer’s disease,” Oxidative Medicine and Cellular Longevity, vol. 2022, Article ID 3012778, 16 pages, 2022.
View at: Publisher Site | Google Scholar
O. Rascol, M. Fabbri, and W. Poewe, “Amantadine in the treatment of Parkinson's disease and other movement disorders,” The Lancet. Neurology, vol. 20, no. 12, pp. 1048–1056, 2021.
View at: Publisher Site | Google Scholar
A. Sauerbier, P. Loehrer, S. T. Jost et al., “Predictors of short-term impulsive and compulsive behaviour after subthalamic stimulation in Parkinson disease,” Journal of Neurology, Neurosurgery, and Psychiatry, vol. 92, no. 12, pp. 1313–1318, 2021.
View at: Publisher Site | Google Scholar
N. L. Rey, S. George, J. A. Steiner et al., “Spread of aggregates after olfactory bulb injection of α-synuclein fibrils is associated with early neuronal loss and is reduced long term,” Acta Neuropathologica, vol. 135, no. 1, pp. 65–83, 2018.
View at: Publisher Site | Google Scholar
A. H. Schapira, K. Chaudhuri, and P. Jenner, “Non-motor features of Parkinson disease,” Nature Reviews Neuroscience, vol. 18, no. 7, pp. 435–450, 2017.
View at: Publisher Site | Google Scholar
S. Lotankar, K. S. Prabhavalkar, and L. K. Bhatt, “Biomarkers for Parkinson's disease: recent advancement,” Neuroscience Bulletin, vol. 33, no. 5, pp. 585–597, 2017.
View at: Publisher Site | Google Scholar
S. N. Rai, V. K. Chaturvedi, P. Singh, B. K. Singh, and M. P. Singh, “Mucuna pruriens in Parkinson's and in some other diseases: recent advancement and future prospective,” Biotech, vol. 10, no. 12, p. 522, 2020.
View at: Publisher Site | Google Scholar
L. Pihlstrøm, C. C. Fan, O. Frei et al., “Genetic stratification of age-dependent Parkinson's disease risk by polygenic hazard score,” Movement disorders, vol. 37, no. 1, pp. 62–69, 2022.
View at: Publisher Site | Google Scholar
S. Bido, S. Muggeo, L. Massimino et al., “Microglia-specific overexpression of α-synuclein leads to severe dopaminergic neurodegeneration by phagocytic exhaustion and oxidative toxicity,” Nature Communications, vol. 12, no. 1, p. 6237, 2021.
View at: Publisher Site | Google Scholar
M. Martinez-Banaclocha, “Proteomic complexity in Parkinson's disease: a redox signaling perspective of the pathophysiology and progression,” Neuroscience, vol. 453, pp. 287–300, 2021.
View at: Publisher Site | Google Scholar
B. Domon and R. Aebersold, “Mass Spectrometry and Protein Analysis,” Science, vol. 312, pp. 212–217, 2006.
View at: Publisher Site | Google Scholar
A. F. Ehigie, F. D. Ojeniyi, A. Aborisade, J. B. Agboola, and L. Ehigie, Transcriptomic analysis of differential gene expression in staphylococcus aureus-induced pneumonia in pediatrics based on microarray analysis, Research Square, 2022.
P. Langfelder and S. Horvath, “WGCNA: an R package for weighted correlation network analysis,” BMC Bioinformatics, vol. 9, no. 1, p. 559, 2008.
View at: Publisher Site | Google Scholar
M. D. Wilkerson and D. N. Hayes, “ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking,” Bioinformatics, vol. 26, no. 12, pp. 1572-1573, 2010.
View at: Publisher Site | Google Scholar
T. A. Taz, K. Ahmed, B. K. Paul et al., “Network-based identification genetic effect of SARS-CoV-2 infections to idiopathic pulmonary fibrosis (IPF) patients,” Briefings in Bioinformatics, vol. 22, no. 2, pp. 1254–1266, 2021.
View at: Publisher Site | Google Scholar
A. Ben-Hur and W. S. Noble, “Kernel methods for predicting protein–protein interactions,” Bioinformatics, vol. 21, Suppl 1, pp. i38–i46, 2005.
View at: Publisher Site | Google Scholar
Z. Wu, L. Wang, Z. Wen, and J. Yao, “Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer,” Scientific Reports, vol. 11, no. 1, p. 3292, 2021.
View at: Publisher Site | Google Scholar
L. Yao, Y. Ye, H. Mao et al., “MicroRNA-124 regulates the expression of MEKK3 in the inflammatory pathogenesis of Parkinson's disease,” Journal of Neuroinflammation, vol. 15, no. 1, pp. 1–19, 2018.
View at: Publisher Site | Google Scholar
L. Yao, Z. Zhu, J. Wu et al., “MicroRNA-124 regulates the expression of p62/p38 and promotes autophagy in the inflammatory pathogenesis of Parkinson's disease,” FASEB journal, vol. 33, no. 7, pp. 8648–8665, 2019.
View at: Publisher Site | Google Scholar
D. W. Huang, B. T. Sherman, Q. Tan et al., “DAVID bioinformatics resources: expanded annotation database and novel algorithms to better extract biology from large gene lists,” Nucleic Acids Research, vol. 35, suppl_2, pp. W169–W175, 2007.
View at: Publisher Site | Google Scholar
D. Szklarczyk, A. L. Gable, K. C. Nastou et al., “The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets,” Nucleic Acids Research, vol. 49, no. D1, pp. D605–D612, 2021.
View at: Publisher Site | Google Scholar
P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003.
View at: Publisher Site | Google Scholar
A. De Roeck, C. Van Broeckhoven, and K. Sleegers, “The role of ABCA7 in Alzheimer's disease: evidence from genomics, transcriptomics and methylomics,” Acta Neuropathologica, vol. 138, no. 2, pp. 201–220, 2019.
View at: Publisher Site | Google Scholar
N. N. Lyssenko and D. Praticò, “ABCA7 and the altered lipidostasis hypothesis of Alzheimer's disease,” Alzheimers Dement, vol. 17, no. 2, pp. 164–174, 2021.
View at: Publisher Site | Google Scholar
M. M. McGregor and A. B. Nelson, “Circuit mechanisms of Parkinson's disease,” Neuron, vol. 101, no. 6, pp. 1042–1056, 2019.
View at: Publisher Site | Google Scholar
L. V. Kalia and A. E. Lang, “Parkinson's disease,” The Lancet, vol. 386, no. 9996, pp. 896–912, 2015.
View at: Publisher Site | Google Scholar
S. Aerts, D. Lambrechts, S. Maity et al., “Gene prioritization through genomic data fusion,” Nature Biotechnology, vol. 24, no. 5, pp. 537–544, 2006.
View at: Publisher Site | Google Scholar
J. Sun, P. Jia, A. H. Fanous et al., “A multi-dimensional evidence-based candidate gene prioritization approach for complex diseases-schizophrenia as a case,” Bioinformatics, vol. 25, no. 19, pp. 2595–6602, 2009.
View at: Publisher Site | Google Scholar
R. Su, X. Liu, G. Xiao, and L. Wei, “Meta-GDBP: a high-level stacked regression model to improve anticancer drug response prediction,” Briefings in Bioinformatics, vol. 21, no. 3, pp. 996–1005, 2020.
View at: Publisher Site | Google Scholar
W. E. Kaminski, E. Orsó, W. Diederich, J. Klucken, W. Drobnik, and G. Schmitz, “Identification of a novel human sterol-sensitive ATP-binding cassette transporter (ABCA7),” Biochemical and Biophysical Research Communications, vol. 273, no. 2, pp. 532–538, 2000.
View at: Publisher Site | Google Scholar
R. Pal, P. C. Tiwari, R. Nath, and K. K. Pant, “Role of neuroinflammation and latent transcription factors in pathogenesis of Parkinson's disease,” Neurological Research, vol. 38, no. 12, pp. 1111–1122, 2016.
View at: Publisher Site | Google Scholar
D. Zhang and G. Childs, “Human ZFM1 protein is a transcriptional repressor that interacts with the transcription activation domain of stage-specific activator protein,” The Journal of Biological Chemistry, vol. 273, no. 12, pp. 6868–6877, 1998.
View at: Publisher Site | Google Scholar
C. Heintz, T. K. Doktor, A. Lanjuin et al., “Splicing factor 1 modulates dietary restriction and TORC1 pathway longevity in _C. elegans_,” Nature, vol. 541, no. 7635, pp. 102–106, 2017.
View at: Publisher Site | Google Scholar
M. Corioni, N. Antih, G. Tanackovic, M. Zavolan, and A. Krämer, “Analysis of in situ pre-mRNA targets of human splicing factor SF1 reveals a function in alternative splicing,” Nucleic Acids Research, vol. 39, no. 5, pp. 1868–1879, 2011.
View at: Publisher Site | Google Scholar
A. Krämer and U. Utans, “Three protein factors (SF1, SF3 and U2AF) function in pre-splicing complex formation in addition to snRNPs,” The EMBO Journal, vol. 10, no. 6, pp. 1503–1509, 1991.
View at: Publisher Site | Google Scholar
J. D. Godavarthi, S. Polk, L. Nunez, A. Shivachar, N. L. Glenn Griesinger, and A. Matin, “Deficiency of splicing factor 1 (SF1) reduces intestinal polyp incidence in Apc (min/)(+) mice,” Biology, vol. 9, no. 11, 2020.
View at: Publisher Site | Google Scholar
L. Yao and S. Zhang, Multimodal Analysis Reveals New Genetic Effectors in Parkinson'S Disease-Related Gene LRRK2 Gly 2019Ser Mutation, Research Square, 2021.

Copyright

Copyright © 2022 Longping Yao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

657

Downloads

711

Citations