Bioinformatics Approach Unravels Potential Crosstalk Between Cytomegalovirus and Epilepsy

Bioinformatics Approach Unravels Potential Crosstalk Between Cytomegalovirus and Epilepsy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics Approach Unravels Potential Crosstalk Between Cytomegalovirus and Epilepsy Salim Al Rashdi, Nabras Al-Mahrami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4546745/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cytomegalovirus is a double-stranded DNA virus that is known to be associated with congenital disorders. Epilepsy is a neurological disorder that happens due to the activation of the inception of neurotransmitters. It is suggested that cytomegalovirus can affect epilepsy since it can reach the brain. This study aims to investigate the molecular crosstalk between epilepsy and Cytomegalovirus infection using a bioinformatics approach. Methods We used gene expression datasets related to each condition retrieved from the public database. Differentially expressed gene analysis has been done on each dataset group separately. The common genes that are significantly expressed in both conditions have been processed into protein-to-protein network analysis and gene enrichment analysis. Results Results showed that 192 common genes were identified across the two conditions. The three genes C CL2, CD44 , and CCL3 have been defined as hub genes in protein-to-protein interaction networks with the highest centralities measures. This suggests the essential roles of these molecules in biological systems. Additionally, these genes are involved in inflammatory processing and immune response. Conclusion We suggest that inflammatory chemokine molecules have potential molecular crosstalk between Cytomegalovirus and Epilepsy. Therefore, more investigations are required to demonstrate the role of each suggested molecule in the association. Cytomegalovirus Epilepsy Gene expression Transcriptomics inflammatory reaction biological network Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Cytomegalovirus (CMV) also known as Human Herpesvirus-5 (HHV-5) is a double-stranded Deoxyribonucleic acid DNA virus that consists of a 235 Kb long genome that belongs to the Herpesviridae family which is an icosahedral capsid enveloped in shape (Yu et al., 2017 ). Infected people are divided into symptomatic and asymptomatic for therapy purposes in which symptomatic individuals are usually immunocompromised and individuals with asymptomatic infection are usually in good health, even though asymptomatic individuals do not show the clinical phenotype of the infection as symptomatic individuals. Still, they have similar genetic expression profiles (Mohit and Mahmoud, 2023 )(Ouellette et al., 2020 ). Patients infected with symptomatic CMV usually suffer from fever, sore throat, fatigue, and swollen glands and sometimes it may cause infectious mononucleosis and hepatitis which are surprisingly similar clinical features as the Epstein-Barr virus that belongs to the same virus family (CDC, 2020 ). In severe immunocompromised individuals, CMV can cause damage to the central nervous system causing encephalitis, myelitis, and Myeloradiculitis (Tselis, 2014 ). Infection with CMV is not restricted to a certain age or gender, even though, the main transition risk factors of CMV are through fluid contact with infected individuals where it can be transmitted through milk feeding and delivery in case of congenital CMV and other fluids like urine, saliva, blood and those who get transplantation (CDC, 2020 ). Transplantation CMV can happen due to the entry of CMV to a latent phase where the virus remains dormant in the infected cell till they are donated to a patient which may lead to the activation of latent CMV, also, it can reactivate in case of immunocompromised situation such as AIDS, chemotherapy or nutrient deficiency (CDC, 2020 ). Cytomegalovirus is usually a mild infection, but in the case of a compromised immune individual, the rate of virus replication reaches a high level that causes serious end-organ disease since CMV can infect a wide range of epithelial and fibroblast cells (Griffiths and Reeves, 2021 ). CMV introduces its genome to the host cell by inducing PH-independent fusion where the virus enters without its envelope inside a fibroblast. Also, CMV can induce endocytosis where the whole virus enters an epithelial cell with its envelope (Griffiths and Reeves, 2021 ). CMV envelope contains glycoprotein M, glycoprotein N, glycoprotein B, glycoprotein H, UL130, and UL131 which work for inducing platelet-derived growth factor-α (PDGFRα), epidermal growth factor receptor (EGFR), Heparan sulfate proteoglycans (HSPG) and integrins (Griffiths and Reeves, 2021 ). Congenital Cytomegalovirus is an infectious condition where infants get infected with CMV through their mothers who are usually unaware of their infectious condition before delivery which leads to congenital defects in the developing brain (CDC, 2020 ). Not all infected babies show signs and symptoms of CMV, however, some infected conditions are symptomatic and show signs at birth such as jaundice, rash, low birth weight, hepatosplenomegaly, and retinitis and some of them experience seizure episodes. Those signs can develop into long-term health problems including vision loss, motor development delay, Microcephaly, seizures, and hearing loss (CDC, 2020 ). Tegument proteins are proteins that exist in the space between the virus capsid and its envelop in all herpesviruses (Hyun et al., 2017 ). Treatment for CMV is given in case of symptomatic congenital cytomegalovirus in the form of drugs such as valganciclovir for improving the development outcome (CDC, 2020 ). Epilepsy (EP) is a neurological non-contagious disorder in the Central Nervous System (CNS) that is caused by repetitive uncontrolled electric rush in the whole brain or in part of it leading to unintended or involuntary movements (WHO, 2023 ). There are 50 million humans affected by epilepsy which is considered to be the most common global neurological disorder (WHO, 2023 ). Epilepsy is classified into two main categories which are focal seizure and generalized seizure, where focal seizure involves specific involuntary movements of the body part. In contrast, generalized seizure involves the whole body (WHO, 2023 ). Essentially, epilepsy is not restricted to a specific age or sex, however, the effect of seizure episodes may vary between males and females, where males generally experience more severe seizure episodes while women experience more seizure fluctuations (Reddy et al., 2021 ). Epileptic patients develop symptoms like loss of consciousness, awareness, disturbances of movements, and loss of vision, also, they tend to physical injuries because of the sudden involuntary movements which may lead to fractures and bruising which can cause psychological problems to the individual (WHO, 2023 ). EP prevalence in Arab countries is 6.9 per 1000 individuals with leading risk factors of parental consanguinity and family history (Idris et al., 2021 ). Clinical manifestation and electroencephalography (EEG) are used to diagnose epilepsy that is caused by different etiologies such as trauma or damage in the brain, congenital abnormalities, encephalitis, and brain tumors like gliomas(WHO, 2023 ), genetic variance and mutations play a role in activating seizure, for example, UBE3A , CDKL5 , and genes that code ion channels in neurons like KCNQ2 (Samanta, 2020 , 2021 )(Alejandro, 2022 ). Infectious agents may develop epilepsy by inducing inflammatory-mediated agent responses that are present in the brain tissue to affect cytokines secretion leading to hyperexcitability (Vezzani et al., 2016 ). Generally, epilepsy is caused by an error in the neurotransmitter receptors such as NMDA and GABA which enhance or inhibit the electrical signals that may lead to seizure episodes (Kapur, 2018 ). People who have epilepsy are treated with surgery in case of focal seizure or by antiepileptic drugs such as oxcarbazepine and carbamazepine for focal and generalized epilepsy consecutively which are selected with consideration of patient clinical history (Kanner and Bicchi, 2022 ). There is notable evidence of the involvement of cytomegalovirus with epilepsy (Corazzi et al., 2024 )(O’Brien et al., 2022 ). For example, Lin et al studied 112 neurological patients from January 2012 to December 2014, the results showed that 96 cases with epilepsy had the expression of pp67-mRNA of CMV. Another research conducted Magnetic Resonance Imaging (MRI) on patients who had symptomatic congenital CMV. Results showed that the second most abnormal finding of MRI was polymicrogyria which is related to epilepsy (Kwak et al., 2018 ). Overall, previous studies could prove that there is an association between cytomegalovirus infection and epilepsy, but the clear pathogenesis relation is still under study. This study aims to investigate the molecular crosstalk between Cytomegalovirus and Epilepsy based on the transcriptomics and biological network approaches. Materials and methods Data collection The transcriptomics datasets were collected from the gene expression omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI) (Clough et al., 2024 ). The keywords “Epilepsy” and “Cytomegalovirus” have been used for dataset research. Results have been limited to samples of homo sapiens only. For more precise data selection, the inclusion criteria have been implemented as follows; i) the dataset must contain samples from disease and control groups, ii) the dataset has to contain at least eight samples, iii) the dataset can be called by R packages ''GEO2R'' or ''DESeq2'', and iv) the expression profiling has been performed by expression microarray or RNA-sequencing experiment. The exclusion criteria were i) samples collected from other species rather than homo sapiens, ii) the dataset without controls, and iii) datasets associated with multiple diseases. The collected transcriptomics data is shown in Table 1 . Table 1 Collected datasets retrieved from the GEO database with the search key terms “Epilepsy” and “Cytomegalovirus” with the defined criteria . S. NO Accession ID Platform Sample count (case/ control) Reference 1 GSE108211 GPL10558 CMV (148/10) (Ouellette et al., 2020 ) 2 GSE206198 GPL18573 CMV (15/15) (Fulkerson et al., 2020 ) 3 GSE17948 GPL8300 CMV (12/4) (Chan, Nogalski and Yurochko, 2009 ; Chan et al., 2010 ) 4 GSE186334 GPL20301 EP (46/22) (Gomes-Duarte et al., 2022 ) 5 GSE134697 GPL16791 EP (17 / 2) (Kjær et al., 2023 ) Total collected samples CMV (175/29), EP (63/24) Differential gene expression analysis Under R Studio, differential gene expression (DGE) was conducted for all retrieved datasets separately. In detail, R Studio is an integrated development environment for the R programming language (Giorgi, Ceraolo and Mercatelli, 2022 ). It provides a collection of different functions as packages. By using limma and DESsq2 packages, DGE was conducted in three main steps. First, datasets were defined into sample and control groups followed by normalization. Then, variance and dispersion measures were calculated for groups of genes in each dataset separately by comparing mean against variance to predict the scatter of samples. Finally, calculates Log Fold Change (logFC) with its p-value for each gene. For visualization purposes, volcano plots have been created for each dataset to provide the significant scattered up and down expressed genes (See Fig. 1 ). Identify common and unique genes All the datasets were filtered to determine significant up or down-expressed genes using p-value = 0.005. This threshold was defined according to the common practice analysis pipeline recommendation. Furthermore, the datasets have been miraged to create two separate datasets for each condition. Additionally, the duplicated genes have been removed. These datasets have been used as input in the Multiple List Comparator Tool from MolBioTools to define the common and unique genes between CMV and EP (MOLBIOTOOLS, 2023 ). Common genes have been defined as the significant up or down-expressed genes in both CMV and EP datasets, while unique genes have been defined as significant up or down-expressed genes in CMV or EP. This technique enabled the characterization of molecules involved in both conditions for more depth analysis. The number of common and unique genes has been presented in the Venn diagram (See Fig. 2 ). Functional enrichment analysis The list of the common genes between CMV and EP has been defined as input for functional enrichment analysis, considering that each gene might be representative of known biological characteristics such as biological processes and molecular functions. This analysis takes into account the overlap between the significantly expressed genes with annotated biological features and ranks them according to their involvement in the entire system. ClusterProfiler package was the main library used for this analysis and the results are presented in Fig. 4 . Protein-to-protein network The network approach was implemented to define the hub gene product that interacts with most of the molecules in the system. The initial step of network construction was to use a list of common genes between CMV and EP as input in the STRING database. This database is an online application that has been commonly used to construct networks by using the extracted gene symbols (Szklarczyk et al., 2019 ). We set the STRING application to retrieve a maximum of 20 “partner” genes for each input gene symbol in the query list to construct networks that contain additional genes that are neighbouring to each gene in the list. The STRING application is a freely available online tool and it is consistently using other biological databases to find the latest associations between genes and proteins. We constructed one network with different types of connections among the genes through text mining, experimental links, database sources, co-expression, neighborhood, gene fusion, and co-occurrence. The constructed network (see Fig. 5) has been downloaded to Cytoscape for topological features analysis. Topologies analysis To investigate the topological aspects of the protein interaction network, CytoHubba plugging in the Cytoscape was used. We calculate the topological features including degree, closeness, and betweenness (Chin et al., 2014 ). We particularly selected these topological parameters because they are the main properties that define the role of the node in the network system. Additionally, they are the most commonly implemented topological parameters for node ranking in network systems. The definition of each parameter is as follows: 1. degree centrality is an essential property that influences a node connection and it is characterized by the number of a node's connections to other nodes in a network. 2. betweenness centrality counts the number of times a node appears to bridge along the shortest path connecting two nodes. 3. closeness centrality is defined based on the reciprocal of the sum of shortest distances between two nodes in a network. The results of this analysis are presented in Table 2 and Fig. 6. Result Differential gene expression analysis Six datasets were retrieved, four CMV and two EP. GES108211, GES241027, and GES17948 were analyzed with GEO2R, while GSE206198, GES186334, and GSE134697 were analyzed with the help of DESeq2 where each dataset was analyzed individually for DEGs (See Fig. 1 ). Merging the significantly expressed gene for each condition and removing the duplication revealed 4296 genes for CMV and 1188 genes for EP. The common genes between CMV and EP were found to be 192 genes (Fig. 2 ). Protein-protein network analysis and hub gene prediction The common genes (n = 192) have been used as input in the STRING database to retrieve the protein-to-protein interaction network (Fig. 3 ). The network contained nodes (yellow and cyan boxes) that represented the gene product. Each node is labeled with a gene symbol. The interaction of the proteins with each other has been represented in a connected line. The cyan boxes are the highly connected nodes representing the hubs. Hubs play an essential role in miniating the integrity of the networks and mostly have crucial functions for the system. In topologies analysis, three different algorithms, namely degree, betweenness and closeness centralities have been used to rank the hubs genes. We ranked the top 10 hubs' genes from the highest to lowest centrality (See Table 2 ). Among the ranked genes CCL2, CD44 and CCL3 are the top three with the highest centrality respectively. Table 2 The top 20 genes from the biological network (common genes of CMV and EP) were analyzed using three different topological methods through CytoHubba plugin. Betweenness Closeness Degree 1 CCL2 CCL2 CD44 2 CD44 CD44 CCL2 3 CD69 CCL3 CCL3 4 KLF6 TLR2 CD163 5 IRF3 CD69 TLR2 6 CD163 CD163 CCR6 7 CCND1 CCR6 CD69 8 LILRB4 CD38 IRF3 9 NR4A2 CCL4 CCND1 10 CD24 CCND1 CD38 Functional enrichment analysis Functional enrichment analysis showed that hub genes are involved in several biological processes and molecular functions according to gene ontology. We plotted the significantly enriched terms based on the p-value, as illustrated in Fig. 4 . Responding to interferon-gamma was the top enriched biological process while CCR chemokine receptor binding was the top molecular function. This analysis showed clearly that most hub genes are involved in biological processes that might be closely associated with crosstalk between CMV and EP. Discussion Using transcriptomics data integrated with network biology analysis has been commonly utilized to identify and reveal the potential biomarkers shared between multiple diseases. This study focuses on using this approach to find out the potential molecular crosstalk between CMV and EP. We retrieved public transcriptomics datasets with strict inclusion and exclusion criteria to achieve optimal representation of conditions. Under DEG analysis, the significant common genes among the two conditions were determined. Subsequently, the common genes have been analyzed in the protein-to-protein network. This robust approach revealed CCL2, CD44 and CCL3 genes as the top three ranked genes. CCL2 gene have been previously reported as proinflammatory cytokines that are expressed highly in monocytes in response to viral infection, cancer, and autoimmune diseases (Abbas et al., 2018 ). Additionally, increased expression of CCL2 was reported previously in patients with EP (Česká et al., 2023 )(Gianella et al., 2017 ). Furthermore, CD44 is a glycoprotein receptor on the surface of blood cells. It works as a cell-cell interaction molecule such as adhesion and migration(Weng et al., 2022 ). CD44 was found to be involved in neuron synopsis in EP individuals (Kruk et al., 2023 ). The activated CMV appears to express CD44 in all T memory cells (Holtappels et al., 2023 ). CCL3 is reported as a cytokine inflammatory mediator for homeostatic and pathological conditions (Da Silva et al., 2017 ). All the fined genes in this study have been strongly associated with the inflammatory process as they have been suggested by enrichment analysis. These findings shed light on the inflammatory process and immune response as potential crosstalk mechanisms between CMV and EP. Moreover, results showed the important roles of the chemokine molecules. Therefore, we recommend further investigating the roles of CCL2, CD44, and CCL3 genes as crosstalk between the two conditions. The power of utilizing a network for crosstalk between diseases has previously been demonstrated by several studies. Specifically, networks were employed to find the molecular crosstalk between COVID-19 and Alzheimer’s disease using microarray and RNA-seq datasets. The study demonstrated the potential of finding hub genes-drugs interaction. However, it is also important to pinpoint some limitations of this study. 1) Lacking wet lab experiment showed the need of more instigations and 2) revealing unconnected network might impact the overall topologies analysis. As future work, we suggest increasing the level of assertions between the nodes of network rather than having physical interaction among the protein. Conclusion In conclusion, we provide evidence for crosstalk between CMV and EP through inflammatory and immune response. CCL2, CD44, and CCL3 genes have been determined as crosstalk molecules. List Of Abbreviations Cytomegalovirus (CMV) Differential gene expression (DGE) Electroencephalography (EEG) Epidermal growth factor receptor (EGFR), Epilepsy (EP) Gene expression omnibus (GEO) Heparan sulfate proteoglycans (HSPG) Human Herpesvirus-5 (HHV-5) Inducing platelet-derived growth factor-α (pdgfrα), Log Fold Change (logFC) The National Centre for Biotechnology Information (NCBI) Declarations Acknowledgment The authors would like to acknowledge Medical Laboratory Sciences Department at Oman College of Health Science for guidelines. Authors’ contributions Salim Al Rashdi drafted the idea as part of graduation project and Nabras Al-Maharami provided the essential bioinformatics guidelines. All authors contributed to the design of the manuscript and edited the final version. Funding No funding sources. Availability of data and material Data sources were shared in this article and new data were created or analyzed in this study are attached Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests There are no competing interests that the authors declare References Abbas AK et al (2018) ‘Revisiting IL-2: Biology and therapeutic prospects.’, Science immunology , 3(25). https://doi.org/10.1126/sciimmunol.aat1482 Alejandro V-G (2022) ‘Epilepsy [Internet]’, in J. Stanislaw and M. Czuczwar (eds) Epilepsy [Internet] . 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Available at: https://doi.org/ https://doi.org/10.1016/B978-0-444-53488-0.00014-6 Vezzani A et al (2016) ‘Infections, inflammation and epilepsy.’, Acta neuropathologica , 131(2), pp. 211–234. https://doi.org/10.1007/s00401-015-1481-5 Wang Y, Li Z (2019) ‘RNA-seq analysis of blood of valproic acid-responsive and non-responsive pediatric patients with epilepsy.’, Experimental and therapeutic medicine , 18(1), pp. 373–383. https://doi.org/10.3892/etm.2019.7538 Weng X et al (2022) ‘The membrane receptor CD44: novel insights into metabolism’, Trends in endocrinology and metabolism: TEM , 33(5), pp. 318–332. https://doi.org/10.1016/J.TEM.2022.02.002 WHO (2023) Epilepsy , World Health Organization . https://www.who.int/news-room/fact-sheets/detail/epilepsy (Accessed: 24 January 2024) Wu T et al (2021) ‘clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.’, Innovation (Cambridge (Mass.)) , 2(3), p. 100141. https://doi.org/10.1016/j.xinn.2021.100141 Yu X et al (2017) ‘Atomic structure of the human cytomegalovirus capsid with its securing tegument layer of pp150.’, Science (New York, N.Y.) , 356(6345). https://doi.org/10.1126/science.aam6892 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4546745","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313723558,"identity":"6177de65-e475-48f5-828d-24a192b86aa4","order_by":0,"name":"Salim Al Rashdi","email":"","orcid":"","institution":"Oman College of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Salim","middleName":"Al","lastName":"Rashdi","suffix":""},{"id":313723559,"identity":"3461df19-5658-4725-93ce-fc0ae814fb82","order_by":1,"name":"Nabras Al-Mahrami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYDACZh4wBSIZH4AYfKRoYTYAMdgIW8MDZ7FJgElCGszZeY99+PGHQUa+//izyq85djJsDMwPH93Ao8WymS95Zm8bA4/BjRyz27LbkoEOYzM2zsGjxeAwjzEDbwNQiwQP223JbcxALTxs0oS0MP75w8ADclix5LZ64rSATWY4kGDG+HHbYWK08CUzy7ZJgPxiLM247TgPGzMhv5w/e5jxzR8be6DDHn78ua3anp+9+eFjfFqgABwj0GhlJqwcARh/kKJ6FIyCUTAKRgwAAMVEO5kW0WIeAAAAAElFTkSuQmCC","orcid":"","institution":"Royal Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nabras","middleName":"","lastName":"Al-Mahrami","suffix":""}],"badges":[],"createdAt":"2024-06-07 14:34:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4546745/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4546745/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58783887,"identity":"4ec67fc6-1232-4ab3-87ae-2ca5aa6a747e","added_by":"auto","created_at":"2024-06-21 05:34:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":347779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots for differentially expressed genes for each dataset. The x-axis depicts the degree of expression by logFC, while the y-axis depicts the confidentiality of expression (P-value).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4546745/v1/9f97694654ade8569b31bd6a.png"},{"id":58784369,"identity":"a4a1d73b-6345-41df-ad15-cc7da9d0da49","added_by":"auto","created_at":"2024-06-21 05:42:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram represents the common and unique genes for CMV and EP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4546745/v1/f9c450e7d9b977dfb7e9c527.png"},{"id":58784368,"identity":"3360201b-6509-4eb9-8dd0-0361bc3aae48","added_by":"auto","created_at":"2024-06-21 05:42:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":367027,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork of protein-to-protein interaction and detected hub genes (from genes common among CMV and EP\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4546745/v1/a0278efe2979ec54a71111fb.png"},{"id":58783885,"identity":"77cf32ea-9472-41d1-a8df-88591ea30d5e","added_by":"auto","created_at":"2024-06-21 05:34:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":236089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e10 gene ontology terms of hub genes shared between CMV and EP.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4546745/v1/9581849e4cc9af453c80d239.png"},{"id":58785454,"identity":"802fd9ca-5958-42a5-9221-120fd731fa23","added_by":"auto","created_at":"2024-06-21 05:58:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1715654,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4546745/v1/48b9ff3f-9afd-4e4a-83ed-a55d27176c19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics Approach Unravels Potential Crosstalk Between Cytomegalovirus and Epilepsy","fulltext":[{"header":"Background","content":"\u003cp\u003eCytomegalovirus (CMV) also known as Human Herpesvirus-5 (HHV-5) is a double-stranded Deoxyribonucleic acid DNA virus that consists of a 235 Kb long genome that belongs to \u003cem\u003ethe Herpesviridae\u003c/em\u003e family which is an icosahedral capsid enveloped in shape (Yu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Infected people are divided into symptomatic and asymptomatic for therapy purposes in which symptomatic individuals are usually immunocompromised and individuals with asymptomatic infection are usually in good health, even though asymptomatic individuals do not show the clinical phenotype of the infection as symptomatic individuals. Still, they have similar genetic expression profiles (Mohit and Mahmoud, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)(Ouellette et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Patients infected with symptomatic CMV usually suffer from fever, sore throat, fatigue, and swollen glands and sometimes it may cause infectious mononucleosis and hepatitis which are surprisingly similar clinical features as the Epstein-Barr virus that belongs to the same virus family (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In severe immunocompromised individuals, CMV can cause damage to the central nervous system causing encephalitis, myelitis, and Myeloradiculitis (Tselis, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInfection with CMV is not restricted to a certain age or gender, even though, the main transition risk factors of CMV are through fluid contact with infected individuals where it can be transmitted through milk feeding and delivery in case of congenital CMV and other fluids like urine, saliva, blood and those who get transplantation (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Transplantation CMV can happen due to the entry of CMV to a latent phase where the virus remains dormant in the infected cell till they are donated to a patient which may lead to the activation of latent CMV, also, it can reactivate in case of immunocompromised situation such as AIDS, chemotherapy or nutrient deficiency (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cytomegalovirus is usually a mild infection, but in the case of a compromised immune individual, the rate of virus replication reaches a high level that causes serious end-organ disease since CMV can infect a wide range of epithelial and fibroblast cells (Griffiths and Reeves, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CMV introduces its genome to the host cell by inducing PH-independent fusion where the virus enters without its envelope inside a fibroblast. Also, CMV can induce endocytosis where the whole virus enters an epithelial cell with its envelope (Griffiths and Reeves, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CMV envelope contains glycoprotein M, glycoprotein N, glycoprotein B, glycoprotein H, UL130, and UL131 which work for inducing platelet-derived growth factor-α (PDGFRα), epidermal growth factor receptor (EGFR), Heparan sulfate proteoglycans (HSPG) and integrins (Griffiths and Reeves, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCongenital Cytomegalovirus is an infectious condition where infants get infected with CMV through their mothers who are usually unaware of their infectious condition before delivery which leads to congenital defects in the developing brain (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Not all infected babies show signs and symptoms of CMV, however, some infected conditions are symptomatic and show signs at birth such as jaundice, rash, low birth weight, hepatosplenomegaly, and retinitis and some of them experience seizure episodes. Those signs can develop into long-term health problems including vision loss, motor development delay, Microcephaly, seizures, and hearing loss (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tegument proteins are proteins that exist in the space between the virus capsid and its envelop in all herpesviruses (Hyun et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Treatment for CMV is given in case of symptomatic congenital cytomegalovirus in the form of drugs such as valganciclovir for improving the development outcome (CDC, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEpilepsy (EP) is a neurological non-contagious disorder in the Central Nervous System (CNS) that is caused by repetitive uncontrolled electric rush in the whole brain or in part of it leading to unintended or involuntary movements (WHO, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). There are 50\u0026nbsp;million humans affected by epilepsy which is considered to be the most common global neurological disorder (WHO, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Epilepsy is classified into two main categories which are focal seizure and generalized seizure, where focal seizure involves specific involuntary movements of the body part. In contrast, generalized seizure involves the whole body (WHO, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Essentially, epilepsy is not restricted to a specific age or sex, however, the effect of seizure episodes may vary between males and females, where males generally experience more severe seizure episodes while women experience more seizure fluctuations (Reddy et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Epileptic patients develop symptoms like loss of consciousness, awareness, disturbances of movements, and loss of vision, also, they tend to physical injuries because of the sudden involuntary movements which may lead to fractures and bruising which can cause psychological problems to the individual (WHO, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). EP prevalence in Arab countries is 6.9 per 1000 individuals with leading risk factors of parental consanguinity and family history (Idris et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Clinical manifestation and electroencephalography (EEG) are used to diagnose epilepsy that is caused by different etiologies such as trauma or damage in the brain, congenital abnormalities, encephalitis, and brain tumors like gliomas(WHO, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), genetic variance and mutations play a role in activating seizure, for example, \u003cem\u003eUBE3A\u003c/em\u003e, \u003cem\u003eCDKL5\u003c/em\u003e, and genes that code ion channels in neurons like \u003cem\u003eKCNQ2\u003c/em\u003e (Samanta, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)(Alejandro, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Infectious agents may develop epilepsy by inducing inflammatory-mediated agent responses that are present in the brain tissue to affect cytokines secretion leading to hyperexcitability (Vezzani et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Generally, epilepsy is caused by an error in the neurotransmitter receptors such as NMDA and GABA which enhance or inhibit the electrical signals that may lead to seizure episodes (Kapur, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). People who have epilepsy are treated with surgery in case of focal seizure or by antiepileptic drugs such as oxcarbazepine and carbamazepine for focal and generalized epilepsy consecutively which are selected with consideration of patient clinical history (Kanner and Bicchi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is notable evidence of the involvement of cytomegalovirus with epilepsy (Corazzi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)(O\u0026rsquo;Brien et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, Lin et al studied 112 neurological patients from January 2012 to December 2014, the results showed that 96 cases with epilepsy had the expression of pp67-mRNA of CMV. Another research conducted Magnetic Resonance Imaging (MRI) on patients who had symptomatic congenital CMV. Results showed that the second most abnormal finding of MRI was polymicrogyria which is related to epilepsy (Kwak et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, previous studies could prove that there is an association between cytomegalovirus infection and epilepsy, but the clear pathogenesis relation is still under study. This study aims to investigate the molecular crosstalk between Cytomegalovirus and Epilepsy based on the transcriptomics and biological network approaches.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe transcriptomics datasets were collected from the gene expression omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI) (Clough et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The keywords \u0026ldquo;Epilepsy\u0026rdquo; and \u0026ldquo;Cytomegalovirus\u0026rdquo; have been used for dataset research. Results have been limited to samples of homo sapiens only. For more precise data selection, the inclusion criteria have been implemented as follows; i) the dataset must contain samples from disease and control groups, ii) the dataset has to contain at least eight samples, iii) the dataset can be called by R packages ''GEO2R'' or ''DESeq2'', and iv) the expression profiling has been performed by expression microarray or RNA-sequencing experiment. The exclusion criteria were i) samples collected from other species rather than homo sapiens, ii) the dataset without controls, and iii) datasets associated with multiple diseases. The collected transcriptomics data is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCollected datasets retrieved from the GEO database with the search key terms \u0026ldquo;Epilepsy\u0026rdquo; and \u0026ldquo;Cytomegalovirus\u0026rdquo; with the defined criteria\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. NO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccession ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample count (case/ control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE108211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL10558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMV (148/10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Ouellette et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE206198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL18573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMV (15/15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Fulkerson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE17948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL8300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMV (12/4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Chan, Nogalski and Yurochko, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Chan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE186334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL20301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEP (46/22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Gomes-Duarte et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE134697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL16791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEP (17 / 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Kj\u0026aelig;r et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal collected samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCMV (175/29), EP (63/24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eUnder R Studio, differential gene expression (DGE) was conducted for all retrieved datasets separately. In detail, R Studio is an integrated development environment for the R programming language (Giorgi, Ceraolo and Mercatelli, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It provides a collection of different functions as packages. By using limma and DESsq2 packages, DGE was conducted in three main steps. First, datasets were defined into sample and control groups followed by normalization. Then, variance and dispersion measures were calculated for groups of genes in each dataset separately by comparing mean against variance to predict the scatter of samples. Finally, calculates Log Fold Change (logFC) with its p-value for each gene. For visualization purposes, volcano plots have been created for each dataset to provide the significant scattered up and down expressed genes (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentify common and unique genes\u003c/h2\u003e \u003cp\u003eAll the datasets were filtered to determine significant up or down-expressed genes using p-value\u0026thinsp;=\u0026thinsp;0.005. This threshold was defined according to the common practice analysis pipeline recommendation. Furthermore, the datasets have been miraged to create two separate datasets for each condition. Additionally, the duplicated genes have been removed. These datasets have been used as input in the Multiple List Comparator Tool from MolBioTools to define the common and unique genes between CMV and EP (MOLBIOTOOLS, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Common genes have been defined as the significant up or down-expressed genes in both CMV and EP datasets, while unique genes have been defined as significant up or down-expressed genes in CMV or EP. This technique enabled the characterization of molecules involved in both conditions for more depth analysis. The number of common and unique genes has been presented in the Venn diagram (See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe list of the common genes between CMV and EP has been defined as input for functional enrichment analysis, considering that each gene might be representative of known biological characteristics such as biological processes and molecular functions. This analysis takes into account the overlap between the significantly expressed genes with annotated biological features and ranks them according to their involvement in the entire system. ClusterProfiler package was the main library used for this analysis and the results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProtein-to-protein network\u003c/h2\u003e \u003cp\u003eThe network approach was implemented to define the hub gene product that interacts with most of the molecules in the system. The initial step of network construction was to use a list of common genes between CMV and EP as input in the STRING database. This database is an online application that has been commonly used to construct networks by using the extracted gene symbols (Szklarczyk et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We set the STRING application to retrieve a maximum of 20 \u0026ldquo;partner\u0026rdquo; genes for each input gene symbol in the query list to construct networks that contain additional genes that are neighbouring to each gene in the list. The STRING application is a freely available online tool and it is consistently using other biological databases to find the latest associations between genes and proteins. We constructed one network with different types of connections among the genes through text mining, experimental links, database sources, co-expression, neighborhood, gene fusion, and co-occurrence. The constructed network (see Fig.\u0026nbsp;5) has been downloaded to Cytoscape for topological features analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTopologies analysis\u003c/h2\u003e \u003cp\u003eTo investigate the topological aspects of the protein interaction network, CytoHubba plugging in the Cytoscape was used. We calculate the topological features including degree, closeness, and betweenness (Chin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We particularly selected these topological parameters because they are the main properties that define the role of the node in the network system. Additionally, they are the most commonly implemented topological parameters for node ranking in network systems. The definition of each parameter is as follows: 1. degree centrality is an essential property that influences a node connection and it is characterized by the number of a node's connections to other nodes in a network. 2. betweenness centrality counts the number of times a node appears to bridge along the shortest path connecting two nodes. 3. closeness centrality is defined based on the reciprocal of the sum of shortest distances between two nodes in a network. The results of this analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eSix datasets were retrieved, four CMV and two EP. GES108211, GES241027, and GES17948 were analyzed with GEO2R, while GSE206198, GES186334, and GSE134697 were analyzed with the help of DESeq2 where each dataset was analyzed individually for DEGs (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Merging the significantly expressed gene for each condition and removing the duplication revealed 4296 genes for CMV and 1188 genes for EP. The common genes between CMV and EP were found to be 192 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein network analysis and hub gene prediction\u003c/h2\u003e \u003cp\u003eThe common genes (n\u0026thinsp;=\u0026thinsp;192) have been used as input in the STRING database to retrieve the protein-to-protein interaction network (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The network contained nodes (yellow and cyan boxes) that represented the gene product. Each node is labeled with a gene symbol. The interaction of the proteins with each other has been represented in a connected line. The cyan boxes are the highly connected nodes representing the hubs. Hubs play an essential role in miniating the integrity of the networks and mostly have crucial functions for the system. In topologies analysis, three different algorithms, namely degree, betweenness and closeness centralities have been used to rank the hubs genes. We ranked the top 10 hubs' genes from the highest to lowest centrality (See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the ranked genes \u003cem\u003eCCL2, CD44\u003c/em\u003e and \u003cem\u003eCCL3\u003c/em\u003e are the top three with the highest centrality respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 20 genes from the biological network (common genes of CMV and EP) were analyzed using three different topological methods through CytoHubba plugin.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCL3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKLF6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIRF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTLR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCR6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCR6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLILRB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIRF3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNR4A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCD38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis showed that hub genes are involved in several biological processes and molecular functions according to gene ontology. We plotted the significantly enriched terms based on the p-value, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Responding to interferon-gamma was the top enriched biological process while CCR chemokine receptor binding was the top molecular function. This analysis showed clearly that most hub genes are involved in biological processes that might be closely associated with crosstalk between CMV and EP.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing transcriptomics data integrated with network biology analysis has been commonly utilized to identify and reveal the potential biomarkers shared between multiple diseases. This study focuses on using this approach to find out the potential molecular crosstalk between CMV and EP. We retrieved public transcriptomics datasets with strict inclusion and exclusion criteria to achieve optimal representation of conditions. Under DEG analysis, the significant common genes among the two conditions were determined. Subsequently, the common genes have been analyzed in the protein-to-protein network. This robust approach revealed CCL2, CD44 and CCL3 genes as the top three ranked genes. CCL2 gene have been previously reported as proinflammatory cytokines that are expressed highly in monocytes in response to viral infection, cancer, and autoimmune diseases (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, increased expression of CCL2 was reported previously in patients with EP (Česk\u0026aacute; et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)(Gianella et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, CD44 is a glycoprotein receptor on the surface of blood cells. It works as a cell-cell interaction molecule such as adhesion and migration(Weng et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). CD44 was found to be involved in neuron synopsis in EP individuals (Kruk et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The activated CMV appears to express CD44 in all T memory cells (Holtappels et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). CCL3 is reported as a cytokine inflammatory mediator for homeostatic and pathological conditions (Da Silva et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). All the fined genes in this study have been strongly associated with the inflammatory process as they have been suggested by enrichment analysis. These findings shed light on the inflammatory process and immune response as potential crosstalk mechanisms between CMV and EP. Moreover, results showed the important roles of the chemokine molecules. Therefore, we recommend further investigating the roles of CCL2, CD44, and CCL3 genes as crosstalk between the two conditions. The power of utilizing a network for crosstalk between diseases has previously been demonstrated by several studies. Specifically, networks were employed to find the molecular crosstalk between COVID-19 and Alzheimer\u0026rsquo;s disease using microarray and RNA-seq datasets. The study demonstrated the potential of finding hub genes-drugs interaction. However, it is also important to pinpoint some limitations of this study. 1) Lacking wet lab experiment showed the need of more instigations and 2) revealing unconnected network might impact the overall topologies analysis. As future work, we suggest increasing the level of assertions between the nodes of network rather than having physical interaction among the protein.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we provide evidence for crosstalk between CMV and EP through inflammatory and immune response. CCL2, CD44, and CCL3 genes have been determined as crosstalk molecules.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eCytomegalovirus (CMV)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDifferential gene expression (DGE)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eElectroencephalography (EEG)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpidermal growth factor receptor (EGFR),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEpilepsy (EP)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGene expression omnibus (GEO)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHeparan sulfate proteoglycans (HSPG)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHuman Herpesvirus-5 (HHV-5)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInducing platelet-derived growth factor-α (pdgfrα),\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLog Fold Change (logFC)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe National Centre for Biotechnology Information (NCBI)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Medical Laboratory Sciences Department at Oman College of Health Science for guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSalim Al Rashdi drafted the idea as part of graduation project and Nabras Al-Maharami provided the essential bioinformatics guidelines. All authors contributed to the design of the manuscript and edited the final version.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sources were shared in this article and new data were created or analyzed in this study are attached\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests that the authors declare\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas AK et al (2018) \u0026lsquo;Revisiting IL-2: Biology and therapeutic prospects.\u0026rsquo;, \u003cem\u003eScience immunology\u003c/em\u003e, 3(25). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciimmunol.aat1482\u003c/span\u003e\u003cspan address=\"10.1126/sciimmunol.aat1482\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlejandro V-G (2022) \u0026lsquo;Epilepsy [Internet]\u0026rsquo;, in J. 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Epilepsy is a neurological disorder that happens due to the activation of the inception of neurotransmitters. It is suggested that cytomegalovirus can affect epilepsy since it can reach the brain. This study aims to investigate the molecular crosstalk between epilepsy and Cytomegalovirus infection using a bioinformatics approach.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe used gene expression datasets related to each condition retrieved from the public database. Differentially expressed gene analysis has been done on each dataset group separately. The common genes that are significantly expressed in both conditions have been processed into protein-to-protein network analysis and gene enrichment analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eResults showed that 192 common genes were identified across the two conditions. The three genes C\u003cem\u003eCL2, CD44\u003c/em\u003e, and \u003cem\u003eCCL3\u003c/em\u003e have been defined as hub genes in protein-to-protein interaction networks with the highest centralities measures. This suggests the essential roles of these molecules in biological systems. Additionally, these genes are involved in inflammatory processing and immune response.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe suggest that inflammatory chemokine molecules have potential molecular crosstalk between Cytomegalovirus and Epilepsy. Therefore, more investigations are required to demonstrate the role of each suggested molecule in the association.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Approach Unravels Potential Crosstalk Between Cytomegalovirus and Epilepsy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-21 05:34:04","doi":"10.21203/rs.3.rs-4546745/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebd72b9d-2248-436f-bd7f-4def042a0ad3","owner":[],"postedDate":"June 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-21T05:34:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-21 05:34:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4546745","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4546745","identity":"rs-4546745","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}