Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

doi:10.21203/rs.3.rs-5052113/v1

The field of computational bioinformatics and systems biology analysis is growing rapidly as a result of advanced bioinformatics tools. Bipolar Disorder (BD) is one of the most serious psychiatric illnesses that affect both adults and adolescent. In recent years the danger of stroke is expanded in people with BD. Stroke and BD diseases are interrelated. Background studies suggest that BD and stroke share a large number of biochemical as well as genetic characteristics. The aim of this study was to explore the association between genetic variations and the two aforementioned disorders.as well as and to construct a PPI network or identical channel. For this reason, common genes are discovered to identify shared pathways. Based on biochemical, molecular, and genetic interactions between shared genes, this study finds the most important hub genes. Observing these associations, the Protein-Protein Interaction (PPI) network, Topological properties analysis, Enrichment Analysis, Co-Expression network, Gene regulatory network (GRN), and also physical interaction network are displayed. Thus, this will help to compare the biochemical and genetic characteristics of BD and Stroke. The interaction of proteins with drug compounds allows for the efficient creation of drugs for this study. Finally chemical experiments may be used to further verify the efficacy of these drugs.

Bipolar Disorder

System Biology

Stroke

Protein-Protein Interaction

Co-expression network

Topological properties

Hub gene

Protein-Drug Interaction

People nowadays suffer from a wide variety of psychological illness, including BD, panic disorder, depression, stress, schizophrenia, stroke and many more. It is also known to as a "psychiatric" or "mental" illness [1]. An increasing number of people throughout the world suffer from mental problems. Because of this, mental illnesses are regarded to be the most lethal condition worldwide [2]. The World Health Organization estimates that around 12% of global infection rates are attributable to behavioral and mental problems. In 2017, the Mental Health Statistical Assessment estimated that 792 million individuals worldwide were dealing with some kind of mental health problem. That's equivalent to 10.7 percent of the global population [3]. Obesity, diabetes mellitus, and cardiovascular disease are the most frequent medical issues in BD patients, and all of these illnesses plus depressed symptoms are stroke risk factors [4]. Thus, there seems to exist a reasonable biological link between BD and stroke.

BD is a significant mental disease that is linked with a number of physical problems that contribute significantly to morbidity and death [5-6]. There have been many studies showing that common medical diseases such as obesity, hypertension, hyperlipidemia, and diabetes mellitus are comorbid with BD, and that these illnesses are all known as being risk factors for stroke [7]. In this paper, two diseases are chosen for analysis. The selection of two distinct disorder disease is made with the goal of determining their connections with one another. When a patient is affected with only one disease, the danger is significantly reduced. When a person is infected with more than one disease, the danger increases exponentially. Specifically, the goal of this observation is to discover a genetic connection between these diseases, as well as the study of the genetic connection between these genes. The preferred two disorders are BD and stroke.

BD affects around 1% of the population, and is one of the leading causes of loss in young people [8]. BD is one of the world's ten most devastating diseases, and also takes away years of good functioning from individuals who are affected by the disease [9]. The death rate for people with BD was a few times that of overall general deaths. In the past few decades, stroke has risen to become the leading cause of mortality in East Asian nations. Stroke may develop when people are under excessive social pressure, which has also been linked to the development of anxiety and depression. Stroke is the second leading cause of mortality as well as the third leading cause of ailment in the world [10]. Stroke kills at least one person every 40 seconds and approximately 800,000 per year. According to the World Health Organization's (WHOs) 1990-2006 research, women die from stroke at a greater rate than males, and 60 percent of occurrences occur when they are over the age of 75. The stroke rate has risen steadily in recent decades among individuals aged 20 to 64, reaching over 25% and rising steadily [11]. Stroke survivors may impose a financial and emotional burden on their family and society [3].

PPIs play a crucial role in the cellular processes and biological functions of every living thing. In the both drug design and systems biology, the study of protein interactions is considered to be the beginning stage. This is because it is through this step that significant information about the functions of proteins can be obtained [12]. Proteins are capable to carry out their tasks in a cell mainly to the cooperation of another protein, as well as the data that is gathered from a PPIs system enhances one's understanding of the protein in concern and the role it plays in the cell [13]. The viewpoint and depiction provided by the PPI network enables us here to obtain a deeper understanding of the genetic linkages that exist between different organisms by way of regulated activities and protein sequences [14]. Protein interactions can assist to a greater understanding of inflammation processes, the development of various medicinal drugs, and the optimization of treatment. PPIs perform an extremely significant role in the regulation of selectivity along signaling pathways. Thus, understanding fundamental cellular control and developing therapies for specific disorders requires an evaluation of the competitive interaction of PPIs in a cell [15]. PPI interaction networks are powerful resources for understanding cellular processes, disease causes, and drug design. Due to the intricacy of the network, PPIs are notoriously difficult to interpret [16].

Bioinformatics has a wide range of applications, some of which include the following: the collection of data, the organization of data, the sequencing of data, the management of huge datasets, the storage and transmission of data, the summarization of DNA, PPI networking, and the scheduling of treatment [17]. Bioinformatics is a cross-disciplinary field that uses both technological and biological data to catalog biological knowledge [18]. Bioinformatics performs a vital role in obtaining incredibly significant information about diseases for the development of drugs. The use of emerging bioinformatics techniques, such as the building of PPI networks and the formulation of drugs, makes a substantial assessment to gene mapping [19].

Determining a genetic connection and protein-drug interaction (PDI) between BD and stroke was one of the most important goals of this research. The unique feature of the study was the identification and subsequent analysis of genes shared by the two disorders [20].

In this paper, we identify and study in depth the genes that are common by the two diseases. After identifying shared genes, we generated protein-protein interactions, topological properties, gene-miRNA connections, protein-drug interactions, and TF-gene interactions. This study also identifies the hub protein that plays a major role in the pathogenesis of these two diseases. A number of studies on these two diseases have demonstrated the importance of bioinformatics resources. Using these resources, our study aims to develop a drug for BD and stroke. At last, the chemical tests will verify the effectiveness of the investigated drug.

In this section, some individual steps are made from the collection of information to the attainment of the optimal goal. Accordingly, the proposed research approach is organized into many subsections. The Fig. 1 illustrate the proposed research technique.

2.1 Gene Collection

To conduct disease analysis, it is necessary to collect the genes related with the disease. There are only a limited number of well-established databases for bioinformatics tools and services. For this study, genes are obtained from NCBI (https://www.ncbi.nlm.nih.gov/gene/), which is an online based platform for the analysis of biological data. It is a vital resource for tools and services in the field of Bioinformatics. The genes are retrieved depending on their weight in ascending order. In the gene collection process, "gene weight" refers to a numerical value assigned to each gene based on its relevance or importance, often determined by expression levels or statistical significance. Higher-weighted genes are prioritized in the selection process, indicating their greater influence in the analysis. Since the development of drugs for humans is the only aim of our study, we have downloaded the genes corresponding to BD, and stroke in Homo sapiens. Utilizing databases like PubMed, GENE Bank, and OMIM for many purposes. Here genes for Stroke and BD diseases have been gathered with the aid of gene NCBI database. After collecting significant genes, the data is then preprocessed as well as screened.

2.2 Preprocessing

The query list of genes were obtained from NCBI gene database for the specific disorders. The genes are retrieved according to their weight in ascending order. In order to do this analysis, it is necessary to acquire the genes that are primarily accountable for Homo sapiens. Since this research is focused on the development of drugs for humans, so these gene products are obtained for Homo sapiens. The selected genes for Homo sapiens are initially downloaded, and the data is imported into Microsoft Excel. After some screening, the data are equipped into spreadsheet files specific to each disease, which are then utilized for further analysis to determine which genes are shared over studied disorders.

2.3 Gene Mining

Gene mining is the process of searching for useful genes and gene sequences in large databases of genetic information. The process typically involves using computer algorithms such as FP tree and sequential pattern method to search for patterns or similarities between the sequences, and then using this information to infer the function of the gene. The primary purpose of gene mining is to identify an important gene, as opposed to maximizing classification accuracy on a test set. R programming is used to find genes that are concordant across the two disorders [25]. The data mining approach is mainly used to create relevant data. All of the genes are gathered and saved in a text file, while another text file includes additional text or genes that are not related to this particular study. Gene mining is one of the most important aspects of this research which will help us to achieve our objective. Any gene mining error might cause us to stray from the perfect outcome. The data mining approach was employed to discover the candidate genes related with BD and Stroke.

2.4 Protein-protein Interaction

Proteins exhibit a range of functions, with one of the most crucial being their interactions with one another [26]. Protein-protein interactions (PPIs) define the connections between various proteins, which arise from multiple biochemical and hydrophobic effects, along with electrostatic forces. These interactions are vital, as proteins play a key role in mediating biological activities. Moreover, proteins influence molecular and cellular processes, enabling us to determine whether organisms are healthy or diseased. PPI networks provide a useful tool for the study of cell functions, disease mechanisms, and the development of drugs. Furthermore, due to the network's complexity, evaluating a PPI is a very complicated job. Cytoscape is a renowned bioinformatics tool which is often used to create PPI networks [26]. In this study, a PPI structure is created using NetworkAnalyst, which is a specialized Bioinformatics application that can be accessed online.

2.5 Hub gene analysis

In this study, we aimed to discover and examine hub genes that are significant to the link between stroke and bipolar disorder (BD). For this analysis, the hub genes that were found were MYD88, APP, ESR1, TP53, CTNNB1, NFKB1, MAPK1, TNFRSF1A, MAPK3, and TNF. These genes were chosen because, as shown by in-depth bioinformatics study, they were significantly involved in both conditions. We used information from several bioinformatics sources to build a Protein-Protein Interaction (PPI) network in order to identify the hub genes. To determine which nodes, or hub genes, had the greatest influence on the network, topological variables like degree, betweenness centrality, and closeness centrality were used to examine the network [27]. These topological measurements shed light on each gene's significance for preserving the PPI network's integrity as well as its possible involvement in the etiology of stroke and BD. Enrichment analysis and an in-depth study at these hub genes' roles in common biochemical and molecular pathways were used to examine the hub genes' biological importance. To comprehend the regulatory mechanisms controlling these hub genes, the co-expression network and gene regulatory network (GRN) were also formed.

2.6 Enrichment Analysis (GO and Pathways)

Comprehensive enrichment analysis (EA) has been employed to look into the classification of the genetic data in order to find important biological pathways and activities connected to the common genes between stroke and bipolar disorder (BD). The online bioinformatics program STRING, which combines data from several functional systems, was used to do enrichment analysis [28]. In order to classify the genes into Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC) and gain insight into their functions in diverse biological situations, Gene Ontology (GO) analysis was carried out [29]. Moreover, to find disproportionately represented pathways in which the common genes are implicated, pathway enrichment analysis was done. This research identified important pathways that contribute to the pathophysiology of both conditions, which helped to clarify the molecular mechanisms behind the comorbidity of stroke and BD. Possible treatment targets are made possible by the enrichment analysis results, which provide more understanding of the biological importance of these genes.

2.7 Gene-miRNA Interaction

MicroRNAs (miRNAs) play an important function in controlling the gene expression. The vast majority of miRNA interactions are still unknown. Gene-miRNA Interaction is a collection and combination of molecular species that jointly influence the number of gene products and indicate the causality of biochemical changes. They demonstrate how molecular grouping incorporates the quality sets outflow guideline that incrementally generates formative instances and enforces the creation of many separation states. It is responsible for regulating the amounts of these gene products. The precision and longevity of the experimental methods used to identify new miRNA targets and validate predicted interactions may vary considerably [30]. MicroRNAs "seed" compatibility, structural permeability, and sequence as well as positional biases determine functional miRNA interaction sites. These factors promote modulatory interactions between RNA-binding proteins [31]. For this research, the NetworkAnalyst tool is used to analyze the relationships between miRNAs and genes.

2.8 TF-gene Interactions

The interaction between the TF and the discovered common genes assesses the effect of the TF on the metabolic pathways as well as expression levels of the genes [32]. Utilizing the NetworkAnalyst platform in order to detect TF-gene interactions with common genes that have been identified. As an online platform for gene expression, NetworkAnalyst also allows users to do meta-analysis on a wide range of species [33]. The ENCODE database is utilized to generate the TF-gene interaction network in the NetworkAnalyst platform. It is crucial to uncover major TF-gene interactions in order to comprehend the activities of pleiotropic global regulators. Hence, a network of transcription factors regulates essential cellular processes [34].

2.9 Identification of drug signature

The aim of this study is to find drugs that are essential for treating stroke and bipolar disorder (BD). The DSigDB database, which has over 22,000 gene sets connected to different pharmacological molecules, was used to forecast drug compounds. DSigDB, which was accessible through Enrichr, made it easier to visualize and analyze gene functions for enrichment, which in turn helped identify protein-drug interactions (PDI). This approach made it possible to anticipate possible medication candidates that could target the molecular pathways involved in both stroke and BD by utilizing gene expression data from the identified hub genes. The combination of these instruments emphasizes how important gene expression data is for developing novel treatment approaches for these related illnesses.

3.1 Gene Collection & Filtering

For this study, all of the genes responsible for BD and stroke are gathered from the NCBI online gene repository. After collecting from NCBI, the total no. of genes for BD are estimated at 743 and for stroke at 1540; the corresponding genes for Homo sapiens are 720 and 853 respectively. The values of the associated risk genes are shown in Table 1.

Table 1. The depiction of the gene collection of targeted disorders is derived from the NCBI repository. After processing and categorizing all of the related genes for Homo sapiens, there are 720 genes for BD and 853 genes for stroke. Table 1 displays the numerical values for the relevant genes.

Diseases	Total number of Gene	Total number of Homo Sapiens
Bipolar Disorder	743	720
Stroke	1540	853

3.2 Intersection of genes and discovery of shared genes

In this step, the link between the two diseases is established. Python programming language is utilized to identify genes associated with BD and stroke in this work. The BD & stroke intersection is used to find the common gene between two diseases. Genes that overlap between BD and Stroke have found 103 similar genes. To keep things simple, just the top 10 most important genes are included in the analysis. APP, ESR1, TP53, CTNNB1, NFKB1, MAPK1, TNFRSF1A, MAPK3, TNF and MYD88 were the top 10 genes. The top 10 genes account for the majority of the risk for developing those diseases. If we properly address these genes, we can mitigate these illnesses [32]. Figure 2 shows a Venn diagram illustrating the connection between the total number of genes and the proportion of shared genes. To begin this inquiry, genes were extracted from a reliable database. Following that, the dataset was subjected to the mining algorithm. Figure 2 demonstrates the findings of the verification inquiry.

3.3 PPI network among common genes

The PPI network is a mechanism for assigning physical interactions between proteins. The PPI network incorporates both active and passive connections between genes and hub proteins. Correlated genes, protein interactions, and common pathways are shown in this diagram. NetworkAnalyst is a powerful web-based intuitive visual analytics tool for analyzing and interpreting gene expression data at the system level [33]. By using the web-based NetworkAnalyzer, the STRING interactome repository is utilized to create SIF files for the network diagram. The STRING database (http://string-db.org) was designed to provide a comprehensive study and integration of active (physical) and passive (functional) protein-protein interactions [34]. The NetworkAnalyst tool is simple to use. The leading 10 concordant genes of APP, TP53, ESR1, CTNNB1, NFKB1, MAPK1, TNFRSF1A, MAPK3, TNF, and MYD88 were then explored using the PPI network. Ten frequently occurring genes for BD and stroke are provided in NetworkAnalyst. The result of NetworkAnalyst for hub genes is shown in Figure 3 below.

3.4 Topological properties

Betweenness centrality, cluster coefficient, topological coefficient and degree are all components of the PPI network. NetworkAnalyst is used to develop the PPI network, which is then used for topological analysis. The topological features of the PPI network in the Cytoscape tool are estimated using the Network Analyzer program in connection with the PPI structure. The Cytoscape Network Analyzer program uses the SIF document in the Cytoscape intuitive tool to determine topological properties. As seen in Table 3, the top 10 weighted genes have distinct topological properties. The different topological features of the PPI network are shown in Figure 4 (A, B, C, D). Figure 4(A) depicts a clustering measurement, which is a feature of the degree to which nodes in a network are clustered together on average. Betweenness centrality is a measuring of a vertex's influence on the information trickles for each pair of vertices, indicating that data travels mostly along the closest paths between them, as seen in Figure 4(B). Take a look at figure 4(C), Closeness centrality is a technique for detecting elements that is really successful at transmitting information over a network. The centrality of a node's closeness indicates its mean distance from all other endpoints. Nodes having a high intimacy score have the minimum distances to all other nodes. Figure 4(D) illustrates, the topological coefficient quantifies the magnitude to which a node links its peers to other nodes. Nodes with one or no neighbors are awarded a topological coefficient of 0.

Table 2. The Cytoscape program was used to determine the topological features of the top 10 significant genes in the PPI network.

Protein Name	Degree	Betweenness Centrality	Closeness Centrality	Clustering Coefficient	Topological Coefficient
APP	1958	0.647375877	0.514682393	8.61E-05	0.001360901
ESR1	798	0.242729279	0.424589499	0.001415081	0.002733945
TP53	659	0.189862713	0.410766817	0.001701943	0.003276949
CTNNB1	330	0.102658892	0.454436971	0.004163213	0.004188482
NFKB1	240	0.055721604	0.382277788	0.006345886	0.007021912
MAPK1	187	0.042237979	0.3906556	0.011270197	0.008300151
TNFRSF1A	167	0.032575869	0.349841323	0.002164346	0.011958279
MAPK3	127	0.019560447	0.363533802	0.013748281	0.011678508
TNF	91	0.014450557	0.364577894	0.011233211	0.015429468
MYD88	87	0.012837737	0.33908046	0.006950013	0.029567975

3.5 Hub gene

This study identified 10 vital hub genes as being essential to the molecular pathway linking Bipolar Disorder (BD) and stroke: APP, ESR1, TP53, CTNNB1, NFKB1, MAPK1, TNFRSF1A, MAPK3, TNF, and MYD88 is shown in figure 5. The generated Protein-Protein Interaction (PPI) network showed notable topological features and high connectedness for these genes. They have a role in important biological processes that are pertinent to the pathophysiology of both conditions, including apoptosis, cell signaling, and inflammation. Notably, their significance in disease pathways is further highlighted by genes such as TP53 and MAPK1, which are renowned for their roles in cell cycle regulation and stress response. These hub genes may serve as indicators or therapeutic targets, and their discovery offers important new information about the molecular interactions between stroke and BD.

3.6 Enrichment Analysis: GO terms and Pathways

GO (Gene ontology) are classified into 3 categories. Biological process, molecular function, and cellular component are the three types of terms used in GO. Molecular function generally relates to the activity that is carried out by a gene at the molecular level, biological process describes the larger cellular or physiological role that is played by the gene, and cellular component corresponds to the part of the cell in which the gene product is responsible for carrying out its function. GO is a key concept of enrichment analysis that is generated by a web-based application like STRING. The findings of the GO investigation are shown in figure 6 and Table 3. A gene set enrichment approach is needed in pathway analysis. Fig 7 and Table 4 shows the pathway analysis that helps genome to pathways link.

Table 3: A detailed analysis was conducted on the association between common genes in GO keywords and GO pathways, along with the accompanying P-values. Significant enrichment of particular biological processes, cellular constituents, and molecular functions linked to the identified genes was found by this research.

Category	GO ID	Term	P-value	Genes
Biological process	GO:0045944	Positive Regulation Of Transcription By RNA Polymerase II	9.41E-10	APP;CTNNB1;ESR1 ;TP53;TNF;NFKB1 ;TNFRSF1A ;MAPK3
	GO:0051972	Regulation Of Telomerase Activity	4.65E-09	CTNNB1;MAPK1 ;TP53;MAPK3
	GO:0071260	Cellular Response To Mechanical Stimulus	6.07E-09	NFKB1;MYD88 ;TNFRSF1A ;MAPK3
	GO:0045893	Positive Regulation Of DNA-templated Transcription	8.76E-09	AP;CTNNB1;ESR1 ;TP53;TNF;NFKB1 ;TNFRSF1A ;MAPK3
	GO:0051091	Positive Regulation Of DNA-binding Transcription Factor Activity	6.48E-08	APP;CTNNB1;ESR1 ;TNF;MYD88
	GO:0019221	Cytokine-Mediated Signaling Pathway	8.06E-08	TP53;TNF;MYD88 ;TNFRSF1A ;MAPK3
	GO:0071675	Regulation Of Mononuclear Cell Migration	1.38E-07	MAPK1;TNF ;MAPK3
	GO:0006357	Regulation Of Transcription By RNA Polymerase II	4.12E-07	APP;CTNNB1; ESR1;TP53;TNF; NFKB1;TNFRSF1A ;MAPK3
	GO:0032212	Positive Regulation Of Telomere Maintenance Via Telomerase	4.43E-07	CTNNB1;MAPK1 ;MAPK3
	GO:0051173	Positive Regulation Of Nitrogen Compound Metabolic Process	4.87E-07	APP TNF NFKB1

Category	GO ID	Term	P-value	Genes
Molecular Function	GO:0004707	MAP Kinase Activity	1.23E-05	MAPK1;MAPK3
	GO:0019902	Phosphatase Binding	1.99E-05	CTNNB1;MAPK1 ;MAPK3
	GO:0032813	Tumor Necrosis Factor Receptor Superfamily Binding	7.85E-05	TNF;MYD88
	GO:0140296	General Transcription Initiation Factor Binding	9.71E-05	ESR1;TP53
	GO:0030331	Nuclear Estrogen Receptor Binding	1.25E-04	CTNNB1;ESR1
	GO:0001067	Transcription Regulatory Region Nucleic Acid Binding	1.57E-04	TP53;TNF;NFKB1
	GO:0001222	Transcription Corepressor Binding	1.92E-04	CTNNB1;ESR1
	GO:1990837	Sequence-Specific Double-Stranded DNA Binding	2.86E-04	ESR1;TP53;TNF ;NFKB1
	GO:0140297	DNA-binding Transcription Factor Binding	3.09E-04	CTNNB1;TP53 ;MAPK3
	GO:0001221	Transcription Coregulator Binding	0.001063621	CTNNB0;ESR0 ;TP53;NFKB1

Category	GO ID	Term	P-value	Genes
Cellular Function	GO:0045121	Membrane Raft	6.81E-05	APP;TNF ;TNFRSF1A
	GO:0000791	Euchromatin	2.10E-04	CTNNB1;ESR1
	GO:0005788	Endoplasmic Reticulum Lumen	3.16E-04	APP;MAPK1 ;MAPK3
	GO:0005769	Early Endosome	3.97E-04	APP;MAPK1 ;MAPK3
	GO:0005901	Caveola	4.19E-04	MAPK1;MAPK3
	GO:0034774	Secretory Granule Lumen	4.32E-04	APP;MAPK1 ;NFKB1
	GO:0044853	Plasma Membrane Raft	7.31E-04	MAPK1;MAPK3
	GO:0005925	Focal Adhesion	7.80E-04	CTNNB1;MAPK1 ;MAPK3
	GO:0030055	Cell-Substrate Junction	8.27E-04	CTNNB1;MAPK1 ;MAPK3
	GO:0005634	Nucleus	0.001782238	CTNNB1;MAPK1 ;ESR1;TP53;NFKB1 ;MYD88;MAPK3

Table 4: A thorough study was conducted to determine the significance of the association between P-values and frequent genes in the KEGG, WikiPathways, Reactome, and BioCarta databases. This study highlighted important biological processes implicated in the disease mechanisms by identifying key pathways that are considerably enriched with the shared genes.

Databases	Pathways	P-value	Genes
KEGG	Hepatitis C	1.89E-13	CTNNB1;MAPK1 ;TP53;TNF;NFKB1 ;TNFRSF1A;MAPK3
	Lipid and atherosclerosis	1.76E-12	MAPK1;TP53;TNF ;NFKB1;MYD88 ;TNFRSF1A;MAPK3
	Human cytomegalovirus infection	2.42E-12	CTNNB1;MAPK1 ;TP53;TNF;NFKB1 ;TNFRSF1A;MAPK3
	Chagas disease	3.13E-12	MAPK1;TNF; NFKB1;MYD88 ;TNFRSF1A;MAPK3
	Shigellosis	4.55E-12	MAPK1;TP53;TNF ;NFKB1;MYD88 ;TNFRSF1A;MAPK3
	Salmonella infection	4.95E-12	CTNNB1;MAPK1 ;TNF;NFKB1; MYD88;TNFRSF1 ;MAPK3
	Toxoplasmosis	5.55E-12	MAPK1;TNF; NFKB1;MYD88 ;TNFRSF1A;MAPK3
	Sphingolipid signaling pathway	8.04E-12	MAPK1;TP53;TNF ;NFKB1;TNFRSF1 ;MAPK3
	MAPK signaling pathway	1.60E-11	MAPK1;TP53;TNF ;NFKB1;MYD88 ;TNFRSF1A;MAPK3
	Apoptosis	2.36E-11	MAPK1;TP53;TNF ;NFKB1;TNFRSF1A ;MAPK3
WikiPathways	IL 18 Signaling Pathway WP4754	1.00E-19	CTNNB1;MAPK1 ;TP53;TNF;NFKB1 ;MYD88;TNFRSF1A ;MAPK3
	CKAP4 Signaling Pathway Map WP5322	1.39E-14	CTNNB1;MAPK1 ;ESR1;TP53 ;TNF ;NFKB1;TNFRSF1A
	A Network Map Of Macrophage Stimulating Protein MSP Signaling WP5353	8.17E-13	CTNNB1;MAPK1 ;TP53;TNF;NFKB1 ;MAPK3
	T Cell Activation SARS CoV 2 WP5098	1.26E-12	MAPK1;TP53;TNF ;NFKB1;MYD88; MAPK3
	TNF Related Weak Inducer Of Apoptosis TWEAK Signaling Pathway WP2036	4.71E-12	CTNNB1;MAPK1 ;TNF;NFKB1; MAPK3
	Alzheimer 39 S Disease And miRNA Effects WP2059	6.72E-12	APP;CTNNB1 ;MAPK1;TNF; NFKB1;TNFRSF1A ;MAPK3
	Alzheimer 39 S Disease WP5124	6.72E-12	APP;CTNNB1 ;MAPK1;TNF ;NFKB1;TNFRSF1A ;MAPK3
	Cardiac Hypertrophic Response WP2795	3.25E-11	MAPK1;TNF ;NFKB1 ; TNFRSF1A ;MAPK3
	RAC1 PAK1 P38 MMP2 Pathway WP3303	9.72E-11	CTNNB1;MAPK1 ;TP53 ;NFKB1 ;MAPK3
	Urotensin II Mediated Signaling Pathway WP5158	1.05E-10	CTNNB1;MAPK1 ;TNF;NFKB1; MAPK3
Reactome	MyD88 Cascade Initiated On Plasma Membrane R-HSA-975871	2.16E-12	APP MAPK1 TP53 NFKB1 MYD88 MAPK3
	Signaling By Interleukins R-HSA-449147	2.82E-12	APP;MAPK1;TP53 ;TNF;NFKB1; MYD88;TNFRSF1A ;MAPK3
	TRAF6 Mediated Induction Of NFkB And MAP Kinases Upon TLR7/8 Or 9 Activation R-HSA-975138	2.95E-12	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	MyD88 Dependent Cascade Initiated On Endosome R-HSA-975155	3.13E-12	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	Toll Like Receptor 7/8 (TLR7/8) Cascade R-HSA-168181	3.32E-12	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	Toll Like Receptor 9 (TLR9) Cascade R-HSA-168138	3.96E-12	APP;MAPK1;TP53 ;NFKB1 ;MYD88 ;MAPK3
	MyD88:MAL(TIRAP) Cascade Initiated On Plasma Membrane R-HSA-166058	5.55E-12	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	Toll Like Receptor 4 (TLR4) Cascade R-HSA-166016	2.17E-11	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	Toll-like Receptor Cascades R-HSA-168898	5.26E-11	APP;MAPK1;TP53 ;NFKB1;MYD88 ;MAPK3
	Cytokine Signaling In Immune System R-HSA-1280215	9.37E-11	APP;MAPK1;TP53 ;TNF;NFKB1 ;MYD88 ;TNFRSF1A;MAPK3
BioCarta	NF-kB Signaling Pathway Homo sapiens h nfkbPathway	1.88E-10	TNF;NFKB1 ;MYD88;TNFRSF1A
	Ceramide Signaling Pathway Homo sapiens h ceramidePathway	1.28E-09	MAPK1;TNF; TNFRSF1A ;MAPK3
	Keratinocyte Differentiation Homo sapiens h keratinocytePathway	9.12E-09	MAPK1;TNF; TNFRSF1A;MAPK3
	Cadmium induces DNA synthesis and proliferation in macrophages Homo sapiens h cdMacPathway	5.02E-08	MAPK1;TNF ;MAPK3
	Role of ERBB2 in Signal Transduction and Oncology Homo sapiens h her2Pathway	2.93E-07	MAPK1;ESR1 ;MAPK3
	Trefoil Factors Initiate Mucosal Healing Homo sapiens h tffPathway	6.37E-07	CTNNB1;MAPK1 ;MAPK3
	Mechanism of Gene Regulation by Peroxisome Proliferators via PPARa Homo sapiens h pparaPathway	1.96E-06	MAPK1;TNF; MAPK3
	Stat3 Signaling Pathway Homo sapiens h stat3Pathway	6.29E-06	MAPK1;MAPK3
	SODD/TNFR1 Signaling Pathway Homo sapiens h soddPathway	8.08E-06	TNF;TNFRSF1A
	Overview of telomerase protein component gene hTert Transcriptional Regulation Homo sapiens h tertpathway	1.01E-05	ESR1;TP53
BioPlanet	Chagas disease	3.53E-12	MAPK1;TNF; NFKB1;MYD88; TNFRSF1A;MAPK3
	Ceramide signaling pathway	1.79E-11	MAPK1;TNF; NFKB1;TNFRSF1A;MAPK3
	Keratinocyte differentiation	3.92E-11	MAPK1;TNF; NFKB1;TNFRSF1A ;MAPK3
	Alzheimer's disease	6.80E-11	APP;MAPK1;TP53 ;TNF;TNFRSF1A ;MAPK3
	Cadmium-induced DNA biosynthesis and proliferation in macrophages	7.47E-11	MAPK1;TNF ;NFKB1;MAPK3
	TGF-beta signaling pathway	1.18E-10	CTNNB1;MAPK1 ;TP53 ;TNF;NFKB1 ;MAPK3
	Leishmaniasis	1.30E-10	MAPK1;TNF ;NFKB1;MYD88 ;MAPK3
	Modulation of interferon signaling by chaperones	2.78E-10	TP53;TNF;NFKB1 ;TNFRSF1A
	MicroRNAs in cardiomyocyte hypertrophy	3.05E-10	CTNNB1;MAPK1; TNF;NFKB1; MAPK3
	NF-kappaB signaling pathway	3.33E-10	TNF;NFKB1;MYD88;TNFRSF1A

3.7 Gene Regulatory Network

The term GRN is used to express the association between genes in order to better comprehend them. We used web-based impactful NetworkAnalyst software to build the GRN. Generally, gene regulatory networks (GRNs) may be split into three categories: gene-miRNA connections, transcription factor-gene interactions, and TF-miRNA co-regulation network. Gene-miRNA interaction and TF-gene interaction are shown in Figures 8(A) and 8(B), respectively [35].

3.8 Co-expression Network and Physical Interaction Pathway

Typically, a co-expression network is an undirected graph; this network revealed a high association between two proteins. Co-expression is the initial stage in inference since it establishes the link between two transcripts that are expressed together. If two genes were identified to interact in PPI research, they are related. These data sets have assisted find out how similar ligands are to protein networks on a large scale. Lignin-based networks, which predict the potential of nearby proteins to engage connected compounds, might supplement genetically orientated gene networks that anticipate the significance of function or diseases. We examine in depth how much genetic redundancy, practical PPIs, co-expression, and genetic disease identification [32] may be made possible by such links between ligand-based proteins. Two or more proteins may be involved in a physical contact, resulting in binary interactions and more complicated proteins [33]. As an example of this, the actual interaction of ligands, which developed in protein families, is the primary mechanism for establishing protein-protein interactions [34]. In this study, GeneMANIA was put to use in order to build networks of physical contact and co-expression for ten genes responsible for the condition. The physical interaction and co-expression network is shown in figure 9.

3.9 Identification of drug signature

Drug signature analysis was carried out using the Enrichr software, which accesses the DSigDB database, in order to find potential drug compounds for Bipolar Disorder (BD) and stroke. The common DEGs found in both disease were utilised in this research. Numerous therapeutic options were identified that may be able to modify the expression of important hub genes that are shared by stroke and BD based on p-value and modified p-value criteria. The capacity of these substances to target the pathways linked to neuroprotection, inflammation, and cell cycle regulation—all of which are crucial to the pathophysiology of these conditions—led to their identification. The results point to a promising direction for future research and clinical application: these medications may be repurposed or further studied for their therapeutic potential in treating the comorbidities of stroke and BD. TABLE 5 displays the most potent medication molecules.

Table 5. Drug-suggested chemicals associated to Stroke, and Bipolar disorder have been identified through analysis of their shared DEGs. Potential therapeutic drugs that target these common DEGs have been identified by this approach, providing intriguing treatment options for a variety of diseases.

Name of drugs	P-value	Adjusted P-value	Genes
ACROLEIN CTD 00005313	1.973573	4.515536	APP;CTNNB1;MAPK1 ESR1;TP53;TNF;NFKB1 MAPK3
N-Acetyl-L-cysteine CTD 00005305	5.247344	6.002962	APP;CTNNB1;MAPK1; ESR1;TP53;TNF; NFKB1; TNFRSF1A;MAPK3
dicumarol CTD 00005515	1.397297	7.992539	MAPK1;ESR1;TP53 TNF;NFKB1;MAPK3
1'-Acetoxychavicol acetate CTD 00002113	1.397297	7.9925399	MAPK1;TP53;TNF NFKB1;TNFRSF1A; MAPK3
AH 23848 CTD 00002080	2.605087	1.192088	CTNNB1;MAPK1 TNF;NFKB1; TNFRSF1A;MAPK3
EINECS 250-892-2 CTD 00001193	9.296261	3.509643	APP;CTNNB1;MAPK1; ESR1;TP53;TNF; NFKB1; TNFRSF1A;MAPK3
LY 294002 CTD 00003061	1.073754	3.509643	APP;CTNNB1;MAPK1; ESR1;TP53;TNF; NFKB1; TNFRSF1A;MAPK3
p'-DDE CTD 00005754	1.911957	4.860620	MAPK1;ESR1;TP53 TNF;NFKB1;MAPK3
flurbiprofen CTD 00005993	1.911957	4.860620	APP;MAPK1;ESR1 TP53;TNF;MAPK3
celecoxib CTD 00003448	2.173026	4.971884	CTNNB1;MAPK1 ESR1;TP53;TNF; NFKB1;MAPK3

In this study, connections between the aforementioned disorders are revealed. One hundred three shared genes from a selection of disorders associated with BD and stroke have been found. A total of 774 nodes and 1092 edges are uncovered in the PPI network analysis. The major purpose of the study is to find a genetic relationship between these specific diseases. The identification of common genes in these two disorders and the detailed analysis of these genes were the work's main innovations. Consequently, we collected genes from the NCBI and extracted common genes found in these disorders. The number of gene collected in NCBI is quite large. For this reason, it is essential to select genes, screening, and computing tasks. After that, the ten most prevalent genes of APP, ESR1, TP53, CTNNB1, NFKB1, MAPK1, TNFRSF1A, MAPK3, TNF and MYD88 were investigated. Utilizing Cytoscape, a PPI network was created and examined to determine the important genes among common genes. PPI network analysis is the most significant portion of the research since it is essential for hub gene identification, module analysis, and drug detection. It is possible for two or perhaps more proteins for physical interactions, leading to binary connections and difficult and complicated proteins [42–43]. Basically, PPI network are formed when ligands make direct physical interaction with one another. Ligands frequently evolve within the areas of two proteins [44]. In the pathways involved in signaling or metabolism, there are some protein interactions that take place with additional ligands, like nucleic acids, lipids, as well as some small compounds [45]. To gain a deeper understanding of the biological system, the topological properties are investigated from the PPI network and discovered an association among degree, cluster coefficients, number of neighbours, betweenness, etc. The outcomes of the topological analysis were aimed at simplifying the interpretation of co-expression as well as pathway analysis in a far less complicated way.

In this research, three kinds of gene regulatory networks were utilized to determine the functional assessment of genomic processes. From the gene–miRNA interaction network analysis, five hub proteins such as TP53, APP, MAPK1, ESR1, and CTNNB1 are considered in the network. In addition, 10 hub proteins such as TNF, NFKB1, TNFRSF1A, CTNNB1, MYD88, TP53, MAPK1 and MAPK3 with 303 connections in the network are included based on the TF–gene interactions network analysis. The findings from various analyses, including pathway, GO, PPI, and TF-gene analyses, provide a detailed depiction of how shared molecular pathways contribute to disease initiation and progression. Qi Hu et al. [41] also identified inflammatory pathways, the ubiquitin-proteasome system, and oxidative stress-related pathways as significant contributors to limb atrophy following stroke.

Lastly, the five most important genes, including APP, ESR1, TP53, CTNNB1, and NFKB1 are revealed according to their degree value. The TP53 gene is one of the most commonly mutated genes found in breast cancer tumors [46]. A genome-wide study revealed an association between ESR1 germline variants and TP53 somatic mutations in breast tumors. Elevated mRNA expression levels of CTNNB1 and NFKB1 cells were observed in a dose- and time-dependent manner after BPA exposure [47]. We investigated numerous interaction network models to comprehend the cell's biological and biomedical systems and gene interactions. The interaction of therapeutic compounds with the genes they were targeting was shown through gene-miRNA and TF-gene networks. Several of these genes were investigated to get a better understanding of how various medical and biological components in a cell and how they are related to each other. These numerous analyses helped us to propose drugs. The drugs that are suggested should be effective in the treatment of BD and stroke.

In this work we have discussed two diseases: BD and Stroke. Two most essential properties, PPI as well as PDI, have been exhaustively examined in this paper. Modeling involves choosing the appropriate datasets, methods, variables, and techniques for effectively preparing data for data mining tasks. To recommend a drug for disease treatment, it is important to understand the affected genes related with the particular disease. For scientific research with respect to more than one disorder, it is important to catch on the genetic relationships that exist between the selected diseases. The present research-built a cross-discussion sub pathway by linking inter-genes to PPI to illustrate the biological relationship between the specified diseases. For the two specified diseases, the PPI network, PDI network, topological features, and co-expressions all play a role in the creation of drugs. Topological features are determined by exploring the PPI network and visualizing the genes associated with the two diseases using Cytoscape software. The co-expression of genes and their physical interaction were demonstrated using GeneMANIA. The interaction on physical pathways of the relevant genes ensured that a common drug design was used for the scheme. This study may also be useful in better understanding the PDI, which will need more research in the areas of bioinformatics and system biology.

Author Contribution

Md. Faruk Hosen, Md. Abul Basar, Mst. Farjana Yasmin wrote the main manuscript text , methodology and whole manuscript.Md. Rakibul Hasan, Muhammad Shahin Uddin supervised and reviewed the manuscript.

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No competing interests reported.

Identify the potential pathways and candidate biomarkers of stroke associated with bipolar disorder: Bioinformatics and system biology approach

Status:

Version 1

Abstract

Figures

1. Introduction

2. Methodology

2.1 Gene Collection

2.2 Preprocessing

2.3 Gene Mining

2.4 Protein-protein Interaction

2.5 Hub gene analysis

2.6 Enrichment Analysis (GO and Pathways)

2.7 Gene-miRNA Interaction

2.8 TF-gene Interactions

2.9 Identification of drug signature

3. Results

4. Discussion

5. Conclusion

Declarations

Author Contribution

References

Additional Declarations

Status:

Version 1