Identifying the interactome of altered ion channels with lipid metabolism in SARS-CoV-2 infected patients in post-COVID-19 era

Identifying the interactome of altered ion channels with lipid metabolism in SARS-CoV-2 infected patients in post-COVID-19 era | 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 Identifying the interactome of altered ion channels with lipid metabolism in SARS-CoV-2 infected patients in post-COVID-19 era John Philip George, K. T. Shreya Parthasarathi, Kiran Bharat Gaikwad, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5224427/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 The Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection continues to expand its horizon through the development of diverse symptoms, particularly concerning long COVID. The patients infected with the SARS-CoV-2 are being reported to develop new symptoms such as brain fog, fatigue, and other symptoms that are not limited to the respiratory system. The SARS-CoV-2 utilizes the human ion channels (HICs) and molecules involved in lipid metabolism from their entry to their egress. Here, to identify molecular alterations in HICs and lipid metabolism-related genes, transcriptomic data of 277 SARS-CoV-2 infected patients were analyzed. 287 HICs and 754 lipid metabolism-related genes were found to be differentially expressed in SARS-CoV-2 infected patients. Further, an interactome of altered HICs and lipid metabolism-related proteins with SARS-CoV-2 proteins was generated. Extensive data mining approach was employed to generate a pathway map highlighting alteration in several pathways including calcium signaling, long-term depression, and cholesterol metabolism in SARS-CoV-2 infected patients. Moreover, 17 potential drugs with known modes of action that interact with 4 altered HICs including inositol 1,4,5-triphosphate (InsP3) receptors and gap junction protein alpha 1 were identified. Most likely, these HICs are potential candidates for drug repurposing in patients infected with SARS-CoV-2 and require further experimental validation. Bioinformatics Infectious disease Pathogen Transmembrane proteins Lipid bilayer Molecular targets Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The SARS-CoV-2 is a positive-sense single-stranded RNA virus with a faster rate of mutation (Markov et al., 2023 ). Various strains of the virus continued from December 2019 to December 2022, sequentially, beginning with the pre-variant of concern and progressing to the variation of concern, which primarily included Alpha, Delta, BA.1, BA.2, BA.5, and BQ.1. The drop in mortality rate lessened the catastrophic stress due to COVID-19 pandemic, however, the post-infectious conditions like long COVID results into new apprehensions. Individuals who were infected with the SARS-CoV-2 virus are at risk of acquiring long COVID (Marjenberg et al., Yong, 2021 ) (Marjenberg et al., 2023 ). As defined by the National Academies of Sciences, Engineering and Medicine, long COVID is an infection-associated chronic condition (IACC) that occurs after a COVID-19 infection that is present for at least 3 months as a continuous relapsing and remitting, or progressive disease state that affects one or more organ systems (MedicalNewsToday, 2024 ). The symptoms that are observed in the post-COVID scenario include fatigue; palpitations; neurological symptoms such as brain fog, insomnia, change in smell or taste, depression, or anxiety; digestive symptoms including diarrhea and stomach pain and other symptoms including irregularity in menstrual cycle, joint or muscle pain (Raveendran et al., 2021 , Yong, 2021 ). This could result from the interaction of viral proteins with other host systemic proteins. A meta-analysis that includes the understanding of viral-host protein may contribute significant insights that could be considered further for the better management of the current condition of long COVID where direct diagnosis and pathophysiology are not known (Davis et al.).SARS-CoV-2 controls the host’s translation machinery for the translating its proteins (Wong and Damania, 2021 ), (Hoenen and Groseth, 2022 ). Right from its entry into the cell, it engages with several host proteins like ACE2, TMPRSS2, and cathepsin along with some other membrane proteins (Jackson et al., 2022 ). Consequently, viruses developed multiple mechanisms for exploiting normal host cell functions (Hoenen and Groseth, 2022 ). One of the most crucial virus-host interactions is the modulation of host ion channel activity by viral proteins (Navarese et al., 2020 ). Ion channels are transmembrane proteins that allow ions to flow passively across the cellular membranes due to their electrochemical gradient. This ion exchange across the membrane generates electrical currents that play a variety of roles, including the generation of membrane potential and cellular activities such as signal transduction, synaptic neurotransmitter release, and apoptosis (Kim, 2014 ). Viruses exploit the cellular environment and replicate inside a host that by relying on the flow of ions into and out of the cell (Gordon et al., 2020 ). Particularly, viruses of small-sized genomes rely on the host genomic machinery for many of the vital functions that they perform by interacting with the membrane proteins (Kim, 2014 ). SARS-CoV-2 utilizes HICs for the release of Ca 2+ ions required for the conformational changes in viral S-protein before entering endosomes and fusion with lysosomal membranes (Li, 2016), (Navarese et al., 2020 ), (Perlman and Netland, 2009 ). This increases cytosolic calcium concentration and promotes viral replication by inhibiting host protein trafficking and viral protein maturation (Jayaseelan and Paramasivam, 2020 ), (Wang et al., 2008 ), (Glebov, 2020 ). Previously, we studied the interaction of HICs with SARS-CoV-2 proteins (Munjal et al., 2022 ). HICs including TRPM4, KCNN4, TRPA1, and GJA1 were observed to play roles in several pathways such as insulin secretion, and gap junctions, among others (Munjal et al., 2022 ). Significant alterations occur in metabolic regulation including lipids and lipoproteins during bacterial, viral, and parasitic infections (Filippas-Ntekouan et al., 2017 ). The role of lipids in viral infection includes membrane fusion, replication, endocytosis, and exocytosis (Abu-Farha et al., 2020 ). It has been found that the patients infected with SARS-CoV-2 have lower levels of cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, and apolipoprotein B and A-I, similar to other infections (Feingold, 2000), (Li et al., 2021 ). With recovery from SARS-CoV-2 infections, the serum lipid levels return to normal (Feingold, 2000). Coronavirus first seizes the intracellular membranes of the host cells to form new compartments known as double-membrane vesicles (DMVs) that are required for viral genome amplification. Viruses require specific phospholipid composition to form the ideal replicative organelles for their replication (Xu and Nagy, 2015 ). DMVs are membranous structures containing viral proteins and an array of captured host factors that facilitate an exclusive lipid micro-environment suitable for the replication of coronavirus inside the host (Knoops et al., 2008 ). To modify host cells and produce lipids for viral envelopes, viruses attack lipid synthesis and signaling (Murillo et al., 2015 ). In addition, lipid biogenesis pathways are affected by receptor-mediated virus entry at the endosomal cell surface (Taube et al., 2010 ), (Chazal and Gerlier, 2003 ). Further, interactions between HICs and lipid metabolism-related proteins result in changes in cellular lipid composition and alterations in various signaling pathways (Maus et al., 2017 ) (Parthasarathi et al., 2023 ). Besides, lipids affect channel gating through direct lipid-protein interactions (Cordero-Morales and Vasquez, 2018 ). Ion channel blockers like Tetrandrine have potentially shown antiviral properties by inhibiting viral replication through the endolysosomal pathway (Grimm and Tang, 2020 ), (Heister and Poston, 2020 ). In similar ways, identifying the interactions between deregulated HICs and lipid metabolism-related proteins would thus aid in understanding the key pathways resulting in dynamic changes observed (Li et al., 2021 ) in the lipid metabolism of patients with history of SARS-CoV-2 infection. This would result in identifying HICs that could act as potential biomarkers in managing post COVID condition in SARS-CoV-2 infected petients (Wu et al., 2020 ). Here, the transcriptomic profiles of 277 patients infected with SARS-CoV-2 and 76 control samples were analyzed and the deregulated HICs and lipid metabolism-related genes in SARS-CoV-2 infection were identified. Protein-protein interaction (PPI) networks were generated to exhibit the interactions between the deregulated HICs and lipid metabolism-related proteins. Furthermore, the PPIs identified between SARS-CoV-2 proteins with the deregulated HICs and lipid metabolism-related proteins indicated a plausible interdependence between them. A pathway map was generated to depict the altered pathways including calcium signaling, long-term depression, and cholesterol metabolism as a result of deregulated proteins. Furthermore, drugs known to activate, inhibit, block, or modulate potential HICs were identified. In the future, experimental studies on these interactions would aid in attaining many more mechanistic insights into the SARS-CoV-2 infection. 2 Materials and methods The overall workflow for the identification of potential HICs and their interaction with lipid metabolism-related genes in SARS-CoV-2 infected patients is depicted in Figure 1. 2.1 Data collection A list of 376 HICs was downloaded from the Human Genome Organization Gene Nomenclature Committee database and 1087 lipid metabolism-related genes were obtained from the literature (Li et al., 2020), (Fernandez et al., 2020), (Dunn et al., 2019), (Moskot et al., 2015), (Meng et al., 2021), (El-Fasakhany et al., 2001), (Russo et al., 2018), (Lutz and Cornett, 2013),(Astudillo et al., 2012),(Sonnweber et al., 2018),(Hanna and Hafez, 2018). RNA sequencing (RNA-seq) paired-end data was downloaded from the National Center for Biotechnology Information-Sequence Read Archive (GSE157103) and The European Bioinformatics Institute-The European Nucleotide Archive (ERP127339) for 277 patients infected with SARS-CoV-2 (Overmyer et al., 2021), (Wu et al., 2021). 76 control samples were obtained from the Genotype-Tissue Expressions (GTEx) portal and GSE157103 dataset. 2.2 Data processing The quality of the reads was checked using FastQC (v0.11.9) (Trivedi et al., 2014), and the good quality reads were aligned to the human reference genome GRCh38.p13 using STAR aligner (v2.7.10a) (Dobin et al., 2013). Gene expression quantification was performed using HTSeq (v1.99.2) (Anders et al., 2015) for each sample. Further, the expression profiles corresponding to HICs and lipid metabolism-related genes were parsed. 2.3 Identification of differentially expressed genes The gene-wise read count data for HICs and lipid metabolism-related genes were used as inputs for DESeq-2 (Love et al., 2014), an R Bioconductor (v4.2.1) package for obtaining differentially expressed genes (DEGs). The samples from the study GSE157103 were treated as technical replicates using collapseReplicates (Love et al., 2014). The parameters used for the identification of DEGs were a log2-fold change of |0.6| and Benjamini Hochberg-adjusted p values < 0.05. 2.4 Enrichment Analysis for Functional Clustering of DEGs Gene set enrichment analysis (GSEA) was used to determine the key biological processes, cellular components, and molecular functions using Enrichr (Chen et al., 2013) for the DEGs. Biological annotations with significant adjusted P-values were selected. Further, extensive data mining was performed to identify potential molecules pertaining to SARS-CoV-2 infection. 2.5 Protein-protein interaction networks 2.5.1 Altered HICs with lipid metabolism-related proteins Protein-protein interaction networks (PPINs) of the differentially expressed HICs and lipid metabolism-related proteins were constructed using the STRING (v12.0) database (Szklarczyk et al., 2021). The interactions with a high confidence and false discovery rate of 1% were visualized in Cytoscape (v3.10.1)(Shannon et al., 2003) and Gephi (v0.10) (Amith et al., 2019). Further, the obtained network was analyzed using NetworkAnalyser, a Cytoscape plugin, and various properties of the network including degree centrality and betweenness centrality were estimated. The top 20 nodes with the highest degree centrality measure were selected as hub nodes in the network. 2.5.2 Critical subnetworks in PPINs using the hub nodes Hub nodes are the proteins with a higher degree centrality, which refers to several interactions between the respective node towards the other nodes/proteins in the network. Molecular Complex Detection (MCODE) (Bader and Hogue, 2003), a Cytoscape plugin, identifies densely connected regions based on prior knowledge of hub nodes from the PPINs obtained from the STRING database. Highly interacting subnetworks from the complex PPINs could be obtained using this plugin. The parameters for maximum depth and k-score were set to 100 and 2, respectively. Similarly, the node score cutoff was set to 0.2. Further, the subnetworks with a minimum of 10 nodes including both HICs and lipid metabolism-related proteins were selected as significant subnetworks. Pathway enrichment analysis for the subnetworks that included both ion channels and lipid metabolism-related proteins was performed using a Cytoscape plugin, ClueGO (v2.5.10) (Bindea et al., 2009). 2.5.3 SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins The binary interactions of SARS-CoV-2 proteins with deregulated human proteins were downloaded from the BioGRID (v4.4.215) database (updated on 31 st October 2022) (Oughtred et al., 2021). The interactions corresponding to HICs and lipid metabolism-related proteins with SARS-CoV-2 proteins were parsed from the list of interactions. Intra-protein interactions of the deregulated HICs and lipid metabolism-related genes interacting with each SARS-CoV-2 protein were generated using the STRING (v12.0) database (Szklarczyk et al., 2021). Thereafter, the interactions were overlaid with SARS-CoV-2 proteins and were visualized in Cytoscape (v3.10) (Shannon et al., 2003). The interactions of SARS-CoV-2 proteins with both deregulated HICs and lipid metabolism-related genes were extracted. 2.6 Generation of pathway map of HICs and lipid metabolism-related proteins The pathways involving the deregulated HICs and lipid metabolism-related proteins were determined using the STRING database. Following that, a comprehensive data mining approach was used to screen studies from the PubMed database referring to changes detected in pathways involving altered HICs and lipid metabolism-related proteins in SARS-CoV-2-infected patients. The interactions between the molecules in pathways including transportation, translocations, inhibitions, and activations were annotated (Zhong et al., 2014), (Parthasarathi et al., 2021). Thereafter, a pathway map was generated using PathVisio (v3.3.0) (Kutmon et al., 2015) in Graphical Pathway Markup Language format. It included pathways that could lead to the most common symptoms observed in SARS-CoV-2-infected patients involving deregulated HICs and lipid metabolism-related proteins. 2.7 Identification of drugs interacting with HICs The list of drugs interacting with differentially expressed HICs that interacted with both lipid metabolism-related proteins and SARS-CoV-2 proteins was parsed from the Drug Gene Interaction database (DGIdb) (Freshour et al., 2021). The drugs with a known mode of interaction with the HICs were depicted using a customized Python script. 3 Results 3.1 Differentially expressed HICs and Lipid metabolism-related genes in SARS-CoV-2-infected patients A list of 287 differentially expressed HICs was identified, out of which, 218 genes were found to be up-regulated and 69 to be down-regulated. Similarly, 754 differentially expressed lipid metabolism-related genes were identified, of which, 509 genes were up-regulated and 245 were down-regulated in SARS-CoV-2-infected patients. 3.2 Functional clustering of DEGs Pathologically significant annotations for groups of deregulated HICs and lipid metabolism-related genes were identified in terms of biological processes, cellular components, and molecular functions that were reported to be altered in SARS-CoV-2 patients. Most gene groups were identified as being enriched with a higher combined score in cholesterol transport and binding; response to salt; neurotransmitter receptor complex, neuron projection, and chemical synaptic transmission; lipid transportation; Ca 2+ , K + , Na + , channel activity; gap junction assembly and channel activity (Figure 2 and Supplementary Figure S1) . 3.3 Densely interconnected subnetworks through PPINs PPINs determined through the STRING database of the differentially expressed HICs and lipid metabolism-related proteins revealed the predicted protein-protein interactions between them (Figure 3A, Supplementary Table S1). Further, the network analyses of the PPINs using NetworkAnalyser (Assenov et al., 2008) resulted in the identification of the hub nodes in the network (Table 1). Hub nodes in a network are highly interconnected nodes. Out of the top 20 selected hub nodes in the network, 1 was HIC and 19 were lipid metabolism-related proteins. 6 of the 19-hub lipid metabolism-related proteins – PRKACA, PRKACB, PRKACG, AKT1, INS, and APOE interacted with HICs (Table 1). Apolipoprotein E (APOE), an important lipid metabolism-related protein, shows a strong association with many neurodegenerative diseases (Shi et al., 2021), (Yang et al., 2023) along with SARS-CoV-2 infection, especially in regards to post-COVID. Interaction between APOE and ACE2 has been reported to block the cellular entry of SARS-CoV-2 while reducing inflammation in patients (Zhang et al., 2022). Additionally, the deregulated APOE was found to be enriched in cholesterol metabolism using GSEA (Zhang et al., 2022), (Mahley, 2016). Likewise, deregulated CACNA1C was found to be interacting with 11 lipid metabolism-related proteins. 47 highly interconnecting subnetworks were obtained by using the MCODE (Supplementary Table S3). 6 subnetworks that included HICs and lipid metabolism-related proteins were filtered out as significant subnetworks (Supplementary Figure S2). Pathway enrichment analysis for these subnetworks using ClueGO resulted in the identification of gap junction, inflammatory mediator regulation of TRP channels, insulin secretion, insulin resistance, taste transduction, cholesterol metabolism, long-term depression, type II diabetes mellitus (Supplementary Figure S2, Supplementary Table S4). Furthermore, pathway enrichment through the STRING database resulted in the identification of 176 pathways out of which 53 pathways included the deregulated HICs and lipid metabolism-related proteins (Supplementary Table S1, S2, and Supplementary Figure S1) and were found to be associated with SARS-CoV-2 infection utilizing extensive data mining. 3.4 PPINs of SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins A list of 28,250 binary interactions between SARS-CoV-2 proteins and human proteins was obtained from the BioGRID database. Of these, 37 deregulated HICs interacted with 23 SARS-CoV-2 proteins resulting in 190 unique interactions between them, and 281 deregulated lipid metabolism-related proteins interacted with 30 SARS-CoV-2 proteins resulting in 1218 unique interactions. 23 SARS-CoV-2 proteins: E, M, S, Nsp1, Nsp2, Nsp3, Nsp4, Nsp5, Nsp6, Nsp8, Nsp9, Nsp13, Nsp14, Nsp15, Orf3a, Orf3b, Orf6, Orf7a, Orf7b, Orf8, Orf9c, Orf10 and Orf14 interacted with both deregulated HICs and lipid metabolism-related proteins (Supplementary Table S5). The structural protein S interacted with 6 deregulated HICs and 67 lipid metabolism-related proteins (Figure 3B) (Supplementary Figure S3 to S6). 3.5 Pathway map of altered HICs and their interactions with lipid metabolism-related proteins A pathway map was constructed using the STRING database and CLueGO for PPINS and subnetworks, to visually represent the pathologically significant altered pathways in patients. Figure 4 depicts the roles of deregulated HICs and lipid metabolism-related proteins in pathways contributing to the loss of taste, dysregulation in long-term depression, insulin secretion, airway inflammation, and pulmonary fibrosis. The pathway map consists of 169 molecules and 232 reactions. 3.6 Drugs interacting with HICs: Out of a total of 376 HICs, 4 HICs namely, GJA1, ITPR2, ITPR3, and KCNB2 interacted with lipid metabolism-related and SARS-CoV-2 proteins; were identified to interact with 17 drugs including quinine, octanol, flufenamic acid, tetraethylammonium, and dalfampridine. These drugs are known to interact with these HICs with known modes of action which range from being activator, antagonist, blocker, or inhibitor (Supplementary figure 6). 4 Discussion SARS-CoV-2 remains an endemic virus, that can potentially infect, hospitalize, and even kill people. The mortality rate from the SARS-CoV-2 virus infection appeared to drop in early 2023, however, the repercussions on afflicted people continue to worry the scientific community. These effects are no longer limited to the respiratory system, but have spread to other systems of the human body, generating a variety of symptoms such as excessive weariness, brain fog, irregular menstrual cycle, fever, and impaired mobility. Such persistence or development of new symptoms in individuals infected with SARS-CoV-2 is usually referred to as post-COVID conditions. A critical molecular investigation of the SARS-CoV-2 infected patients stands to be a promising approach to gain insights into the molecular changes taking place in the patients with a history of SARS-CoV-2 infection. The consequences of viral exploitation on the host transcriptome were investigated, and changes in the transcriptome of SARS-CoV-2 infected patients were studied. This revealed differences in the expression of several HICs and lipid metabolism-related genes in patients. Pathologically significant annotated groups of altered genes that may cause pathology in patients were found to be enriched using GSEA. Annotations - in terms of biological processes, cellular components, and molecular functions that highlighted ion channels transport and activity; cholesterol transport and binding; insulin response, gap junction assembly, and activity; lipid transportation and activation were obtained through this study. Further, PPINs depicted the interactions between the HICs and lipid metabolism-related proteins and led to identification of pathways. Interesting findings in the form of overlapping pathways depicted the deregulation of pathways such as insulin secretion, calcium signaling, gap junction, cholesterol metabolism, inflammatory mediator regulation of TRP channels, long-term depression, gap junction, renin secretion, and apelin signaling, type II diabetes mellitus, taste transduction, GABAergic synapse, serotonergic synapse, cAMP signaling pathway, RAS signaling pathway, cortisol synthesis and secretion, pancreatic secretion, thyroid hormone synthesis, glucagon signaling pathway and other pathologically critical pathway in SARS-CoV-2 infection. Moreover, these findings correlated with our previous study (Munjal et al., 2022 ). Furthermore, a pathway map consisting of the pathways including long-term depression, cholesterol metabolism, and type II diabetes mellitus was generated. In addition, calcium signaling, inflammatory mediator regulation of TRP channels, insulin secretion, and taste transduction were also represented in the pathway map. Here, we highlight the pathophysiology of following pathways: • Long-term depression Persistence or development of new symptoms in patients infected with SARS-CoV-2 infection referred to as post-COVID affects various organ systems including the central and peripheral nervous system (Zawilska and Kuczynska, 2022 ). Several patients also experience sleep disorders and depression with other nervous breakdowns (Zawilska and Kuczynska, 2022 ), (Olgun Yildizeli et al., 2023 ). HICs including the InsP3 receptor family, and phospholipase A2, among others (Supplementary Table S2) were identified to be deregulated in patients. HICs play a critical role by connecting the inferior olivary neurons in the medulla oblongata through gap junctions (Leznik and Llinas, 2005 ) (also identified to be deregulated as a pathway), maintaining their membrane potential (Llinas and Yarom, 1986 ). A decrease in the diffusion of AMPAR, a ligand-gated ion channel, at the synapses of the neurons could be a result of a rise in the intracellular calcium, which is again a central element in the synaptic plasticity (Henley and Wilkinson, 2013 ), (Anggono and Huganir, 2012 ). Our study has identified these ion channels namely CACNA1A, and ITPRs which are responsible for maintaining the intracellular calcium levels, to be deregulated. • Cholesterol metabolism Alterations in HICs are known to contribute in the deregulation of cholesterol metabolism (Cure and Cumhur Cure, 2021 ), (Maxfield and Wustner, 2002 ), (Craig et al., 2023 ). Cholesterol-enriched lipid rafts serve as a platform for entry of SARS-CoV-2 by endocytosis (Palacios-Rapalo et al., 2021 ). It is known to recruit receptors like ACE2, and transmembrane serine protease 2 for their interaction with viral spike-protein (Palacios-Rapalo et al., 2021 ), describing cholesterol levels are crucial for viral penetration (Kowalska et al., 2022 ). Lower levels of high-density lipoprotein cholesterol (HDL-C) are associated with increased vulnerability to SARS-CoV-2 infection along with other infections, whereas elevated HDL-C levels are linked to reduced susceptibility to these conditions (Kocar et al., 2021 , Kowalska et al., 2022 ). Voltage-dependent anion channels (VDACs), are located on the mitochondrial membrane and facilitate the transfer of metabolites and ions in the mitochondria (Varughese et al., 2021 ), (Campbell and Chan, 2007 ). Its alteration results in the alteration of cholesterol synthesis and transport (Varughese et al., 2021 ). VDACs, apolipoproteins, among other molecules (Supplementary Table S2) were found to be deregulated in the patients. The pathway map highlights the alteration in pathophysiologically critical pathways in the context of SARS-CoV-2 infection and the role of deregulated HICs and lipid metabolism-related proteins in it. Some of these pathways were also identified in our previous study (Munjal et al., 2022 ) highlighting the importance of network-based approaches. 4 out of 376 HICs, that interact with lipid metabolism and SARS-CoV-2 proteins, were identified to interact with 17 drug molecules using DGIdb with a known mode of action. Gap Junction Protein Alpha 1 (GJA1) was identified to interact with Caveolin-1 (CAV1), a lipid metabolism-related protein, and seven SARS-CoV-2 proteins (M, nsp4, nsp6, ORF14, ORF3b, ORF7a, ORF7b). CAV1, which is an integral membrane protein, is critically involved in regulating the blood-brain barrier (Huang et al., 2018 ). Several studies report the upregulation of CAV1 in the forebrain of SARS-CoV-2-infected patients (Green et al., 2022 ), (Premkumar and Sajitha Lulu, 2023 ). Upregulation of CAV1 further results in increased expression of vascular cell adhesion molecule-1 (VCAM-1) and CD3 + T cell infiltration of the hippocampus, a region responsible for memory and short-term learning. This cascading upregulation of proteins was observed in SARS-CoV-2 infection, which contributed to neuroinflammatory symptoms including deficiency in learning and memory (Trevino et al., 2023 ). Knockout studies on mice with CAV1 deficiency showed protection against neuroinflammatory symptoms during SARS-CoV-2 infection (Gioiosa et al., 2008 ), (Trushina et al., 2006 ). CAV1 which interacted with GJA1, was also found to be upregulated in SARS-CoV-2-infected patients. Langlois and colleagues reported the direct binding and interdependence between GJA1 and CAV1 (Langlois et al., 2008 ). Moreover, potential drug molecules like Carbenoxolone, Octanol, and Flufenamic acid were found to interact with GJA1 (Fig. 7). Most likely, GJAI could be one of the potential target molecules for drug repurposing. Likewise, we identified drugs interacting with other three significantly deregulated HICs namely, Inositol 1,4,5-Trisphosphate Receptor Type 2 (ITPR2), Inositol 1,4,5-Trisphosphate Receptor Type 2 (ITPR3), and Potassium Voltage-Gated Channel Subfamily B Member 2 (KCNB2) (Supplementary Table S6 and S7) identified to be interacting with lipid metabolism-related and SARS-CoV-2 proteins. Similarly, drugs that interact with HICs with known functions could be studied further to understand their physiological and therapeutic roles in SARS-CoV-2. 5 Conclusion In conclusion, a transcriptomic analysis of 277 SARS-CoV-2 infected patients revealed alteration of 1041 differentially expressed HICs and lipid metabolism-related genes in the patients. GSEA in combination with strong protein-protein interactions between these groups of proteins led to the enrichment of pathologically critical biological processes and pathways like – cholesterol metabolism, long-term depression, and taste transduction, among others. Further, we identified 17 drugs interacting with 4 potential HICs ( GJA1 , ITPR2 , ITPR3 , and KCNB2 ) that interact with lipid metabolism and SARS-CoV-2 proteins. Targeting them, with further experimental validations, by the means of drug repurposing could aid in better management of the disease condition. Declarations Acknowledgments We would like to thank the authors of the manuscripts for making the datasets used in this study publicly available. J.S. is thankful for funding from the ICMR, Government of India (ICMR Ref No. BMI/12(116)2021 and ICMR Ref No. BMI/12(95)/2021) and Council of Scientific and Industrial Research (CSIR), Government of India [37WS(0114)/2023-24/EMR-II/ASPIRE].. K. T. S. P. is supported by ICMR, Government of India (ICMR Ref No. BMI/12(95)2021), J. P. G. is supported by ICMR, Government of India (ICMR Ref No. BMI/12(116)/2021), K. B. G. is supported by the ICMR, Government of India (ICMR Ref No. BMI/Adhoc/28/2022-23). This work was also supported by a grant from DBT/Wellcome Trust India Alliance entitled “Center for Rare Disease Diagnosis, Research, and Training” (IA/CRC/20/1/600002) to A.P. CRediT authorship contribution statement John Philip George: Methodology, Investigation, Data curation, Visualization, Writing- Original draft preparation. K. T. Shreya Parthasarathi: Methodology, Visualization, Investigation. Kiran Bharat Gaikwad: Writing- Reviewing and Editing. Shweta Rana: Writing- Reviewing and Editing. Vibha Gupta: Resources, Supervision. Punit Kaur : Writing- Reviewing and Editing. Akhilesh Pandey: Supervision, Writing- Reviewing and Editing, Funding acquisition. Harpreet Singh: Supervision, Writing- Reviewing and Editing. Jyoti Sharma: Conceptualization, Methodology, Investigation, Writing- Reviewing and Editing, Funding acquisition, Supervision. Author(s) disclosure (Conflict of interest) statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding statement This work was funded by the Indian Council of Medical Research (ICMR), Government of India (ICMR Ref No. BMI/12(116)/2021). Data availability statement Publicly available datasets were analyzed in this study. The data can be accessed using the following resources: NCBI SRA (https://www.ncbi.nlm.nih.gov/sra/?term=), ENA (https://www.ebi.ac.uk/ena/browser/home), GTEx portal (https://gtexporatal.org/home/datasets/) accessed on 2 August 2022. Customized R scripts used for identification of differentially expressed genes and COVID-19 ICs-LMG Pathway reaction data are available in the GPML format through the GitHub repository via the following URLs: (https://github.com/js-iob/COVID-19_ICs_LMG/blob/main/Differential_expression_RNA-Seq.R, https://github.com/js-iob/COVID-19_ICs_LMG/blob/main/COVID-19_ICs_LMG_PathwayMap.gpml) accessed on 3 June 2023. References Abu-Farha M, Thanaraj TA, Qaddoumi MG et al (2020) The Role of Lipid Metabolism in COVID-19 Virus Infection and as a Drug Target. Int J Mol Sci 21(10). 10.3390/ijms21103544 Amith MT, Fujimoto K, Tao C, NET-EXPO: (2019) A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs. HCI Int 2019 Posters 2019;1034:3–12; 10.1007/978-3-030-23525-3_1 Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. 10.1093/bioinformatics/btu638 Anggono V, Huganir RL (2012) Regulation of AMPA receptor trafficking and synaptic plasticity. Curr Opin Neurobiol 22(3):461–469. 10.1016/j.conb.2011.12.006 Assenov Y, Ramirez F, Schelhorn SE et al (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282–284. 10.1093/bioinformatics/btm554 Astudillo AM, Balgoma D, Balboa MA et al (2012) Dynamics of arachidonic acid mobilization by inflammatory cells. Biochim Biophys Acta 1821(2):249–256. 10.1016/j.bbalip.2011.11.006 Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. 10.1186/1471-2105-4-2 Bindea G, Mlecnik B, Hackl H et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8):1091–1093. 10.1093/bioinformatics/btp101 Campbell AM, Chan SH (2007) The voltage dependent anion channel affects mitochondrial cholesterol distribution and function. Arch Biochem Biophys 466(2):203–210. 10.1016/j.abb.2007.06.012 Chazal N, Gerlier D (2003) Virus entry, assembly, budding, and membrane rafts. Microbiol Mol Biol Rev 67(2):226–237 table of contents. 10.1128/MMBR.67.2.226-237.2003 Chen EY, Tan CM, Kou Y et al (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:128. 10.1186/1471-2105-14-128 Cordero-Morales JF, Vasquez V (2018) How lipids contribute to ion channel function, a fat perspective on direct and indirect interactions. Curr Opin Struct Biol 51:92–98. 10.1016/j.sbi.2018.03.015 Craig M, Yarrarapu SNS, Dimri M, Biochemistry (2023) Cholesterol. In: StatPearls. Treasure Island (FL) Cure E, Cumhur Cure M (2021) Strong relationship between cholesterol, low-density lipoprotein receptor, Na(+)/H(+) exchanger, and SARS-COV-2: this association may be the cause of death in the patient with COVID-19. Lipids Health Dis 20(1):179. 10.1186/s12944-021-01607-5 Davis HE, McCorkell L, Vogel JM et al (2023) Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 21(3):133–146. 10.1038/s41579-022-00846-2 Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. 10.1093/bioinformatics/bts635 Dunn TM, Tifft CJ, Proia RL (2019) A perilous path: the inborn errors of sphingolipid metabolism. J Lipid Res 60(3):475–483. 10.1194/jlr.S091827 El-Fasakhany FM, Uchimura K, Kannagi R et al (2001) A novel human Gal-3-O-sulfotransferase: molecular cloning, characterization, and its implications in biosynthesis of (SO(4)-3)Galbeta1-4(Fucalpha1-3)GlcNAc. J Biol Chem 276(29):26988–26994. 10.1074/jbc.M100348200 Feingold KR et al (2000) Lipid and Lipoprotein Levels in Patients with COVID-19 Infections. In: Endotext. (Feingold KR, Anawalt B, Boyce A, eds.) South Dartmouth (MA) Fernandez LP, Gomez de Cedron M, Ramirez de Molina A (2020) Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment. Front Oncol 10:577420. 10.3389/fonc.2020.577420 Filippas-Ntekouan S, Liberopoulos E, Elisaf M (2017) Lipid testing in infectious diseases: possible role in diagnosis and prognosis. Infection 45(5):575–588. 10.1007/s15010-017-1022-3 Freshour SL, Kiwala S, Cotto KC et al (2021) Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res 49(D1):D1144–D1151. 10.1093/nar/gkaa1084 Gioiosa L, Raggi C, Ricceri L et al (2008) Altered emotionality, spatial memory and cholinergic function in caveolin-1 knock-out mice. Behav Brain Res 188(2):255–262. 10.1016/j.bbr.2007.11.002 Glebov OO (2020) Understanding SARS-CoV-2 endocytosis for COVID-19 drug repurposing. FEBS J 287(17):3664–3671. 10.1111/febs.15369 Gordon DE, Jang GM, Bouhaddou M et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583(7816):459–468. 10.1038/s41586-020-2286-9 Green R, Mayilsamy K, McGill AR et al (2022) SARS-CoV-2 infection increases the gene expression profile for Alzheimer's disease risk. Mol Ther Methods Clin Dev 27:217–229. 10.1016/j.omtm.2022.09.007 Grimm C, Tang R (2020) Could an endo-lysosomal ion channel be the Achilles heel of SARS-CoV2? Cell Calcium 88:102212. 10.1016/j.ceca.2020.102212 Hanna VS, Hafez EAA (2018) Synopsis of arachidonic acid metabolism: A review. J Adv Res 11:23–32. 10.1016/j.jare.2018.03.005 Heister PM, Poston RN (2020) Pharmacological hypothesis: TPC2 antagonist tetrandrine as a potential therapeutic agent for COVID-19. Pharmacol Res Perspect 8(5):e00653. 10.1002/prp2.653 Henley JM, Wilkinson KA (2013) AMPA receptor trafficking and the mechanisms underlying synaptic plasticity and cognitive aging. Dialogues Clin Neurosci 15(1):11–27. 10.31887/DCNS.2013.15.1/jhenley Hoenen T, Groseth A (2022) Virus-Host Cell Interact Cells 11(5). 10.3390/cells11050804 Huang Q, Zhong W, Hu Z et al (2018) A review of the role of cav-1 in neuropathology and neural recovery after ischemic stroke. J Neuroinflammation 15(1):348. 10.1186/s12974-018-1387-y Jackson CB, Farzan M, Chen B et al (2022) Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol 23(1):3–20. 10.1038/s41580-021-00418-x Jayaseelan VP, Paramasivam A (2020) Repurposing calcium channel blockers as antiviral drugs. J Cell Commun Signal 14(4):467–468. 10.1007/s12079-020-00579-y Kim JB, Channelopathies (2014) Korean J Pediatr 57(1):1–18. 10.3345/kjp.2014.57.1.1 Knoops K, Kikkert M, Worm SH et al (2008) SARS-coronavirus replication is supported by a reticulovesicular network of modified endoplasmic reticulum. PLoS Biol 6(9):e226. 10.1371/journal.pbio.0060226 Kocar E, Rezen T, Rozman D (2021) Cholesterol, lipoproteins, and COVID-19: Basic concepts and clinical applications. Biochim Biophys Acta Mol Cell Biol Lipids 1866(2):158849. 10.1016/j.bbalip.2020.158849 Kowalska K, Sabatowska Z, Forycka J et al (2022) The Influence of SARS-CoV-2 Infection on Lipid Metabolism-The Potential Use of Lipid-Lowering Agents in COVID-19 Management. Biomedicines 10(9). 10.3390/biomedicines10092320 Kutmon M, van Iersel MP, Bohler A et al (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11(2):e1004085. 10.1371/journal.pcbi.1004085 Langlois S, Cowan KN, Shao Q et al (2008) Caveolin-1 and – 2 interact with connexin43 and regulate gap junctional intercellular communication in keratinocytes. Mol Biol Cell 19(3):912–928. 10.1091/mbc.e07-06-0596 Leznik E, Llinas R (2005) Role of gap junctions in synchronized neuronal oscillations in the inferior olive. J Neurophysiol 94(4):2447–2456. 10.1152/jn.00353.2005 Li F, Structure, Function, Evolution of Coronavirus Spike Proteins (2016) Annu Rev Virol 3(1):237–261. 10.1146/annurev-virology-110615-042301 Li J, Li Q, Su Z et al (2020) Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma. Lipids Health Dis 19(1):222. 10.1186/s12944-020-01390-9 Li Y, Zhang Y, Lu R et al (2021) Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta 517:66–73. 10.1016/j.cca.2021.02.011 Llinas R, Yarom Y (1986) Oscillatory properties of guinea-pig inferior olivary neurones and their pharmacological modulation: an in vitro study. J Physiol 376:163–182. 10.1113/jphysiol.1986.sp016147 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. 10.1186/s13059-014-0550-8 Lutz CS, Cornett AL (2013) Regulation of genes in the arachidonic acid metabolic pathway by RNA processing and RNA-mediated mechanisms. Wiley Interdiscip Rev RNA 4(5):593–605. 10.1002/wrna.1183 Mahley RW (2016) Central Nervous System Lipoproteins: ApoE and Regulation of Cholesterol Metabolism. Arterioscler Thromb Vasc Biol 36(7):1305–1315. 10.1161/ATVBAHA.116.307023 Marjenberg Z, Leng S, Tascini C et al (2023) Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis. Sci Rep 13(1):15332. 10.1038/s41598-023-42321-9 Markov PV, Ghafari M, Beer M et al (2023) The evolution of SARS-CoV-2. Nat Rev Microbiol 21(6):361–379. 10.1038/s41579-023-00878-2 Maus M, Cuk M, Patel B et al (2017) Store-Operated Ca(2+) Entry Controls Induction of Lipolysis and the Transcriptional Reprogramming to Lipid Metabolism. Cell Metab 25(3):698–712. 10.1016/j.cmet.2016.12.021 Maxfield FR, Wustner D (2002) Intracellular cholesterol transport. J Clin Invest 110(7):891–898. 10.1172/JCI16500 MedicalNewsToday (2024) https://www.medicalnewstoday.com/articles/new-definition-of-long-covid-aims-to-offer-clarity-direction Meng Q, Hu X, Zhao X et al (2021) A circular network of coregulated sphingolipids dictates lung cancer growth and progression. EBioMedicine 66:103301. 10.1016/j.ebiom.2021.103301 Moskot M, Jakobkiewicz-Banecka J, Smolinska E et al (2015) Activities of genes controlling sphingolipid metabolism in human fibroblasts treated with flavonoids. Metab Brain Dis 30(5):1257–1267. 10.1007/s11011-015-9705-x Munjal NS, Sapra D, Parthasarathi KTS et al (2022) Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods. Pathogens 11(2). 10.3390/pathogens11020259 Murillo A, Vera-Estrella R, Barkla BJ et al (2015) Identification of Host Cell Factors Associated with Astrovirus Replication in Caco-2 Cells. J Virol 89(20):10359–10370. 10.1128/JVI.01225-15 Navarese EP, Musci RL, Frediani L et al (2020) Ion channel inhibition against COVID-19: A novel target for clinical investigation. Cardiol J 27(4):421–424. 10.5603/CJ.a2020.0090 Olgun Yildizeli S, Kocakaya D, Saylan YH et al (2023) Anxiety, Depression, and Sleep Disorders After COVID-19 Infection. Cureus 15(7):e42637. 10.7759/cureus.42637 Oughtred R, Rust J, Chang C et al (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30(1):187–200. 10.1002/pro.3978 Overmyer KA, Shishkova E, Miller IJ et al (2021) Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Syst. ;12(1):23–40 e27; 10.1016/j.cels.2020.10.003 Palacios-Rapalo SN, De Jesus-Gonzalez LA, Cordero-Rivera CD et al (2021) Cholesterol-Rich Lipid Rafts as Platforms for SARS-CoV-2 Entry. Front Immunol 12:796855. 10.3389/fimmu.2021.796855 Parthasarathi KTS, Mandal S, George JP et al (2023) Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma. Front Mol Biosci 10:1201459. 10.3389/fmolb.2023.1201459 Parthasarathi KTS, Munjal NS, Dey G et al (2021) A pathway map of signaling events triggered upon SARS-CoV infection. J Cell Commun Signal 15(4):595–600. 10.1007/s12079-021-00642-2 Perlman S, Netland J (2009) Coronaviruses post-SARS: update on replication and pathogenesis. Nat Rev Microbiol 7(6):439–450. 10.1038/nrmicro2147 Premkumar T, Sajitha Lulu S (2023) Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach. Front Med (Lausanne) 10:1151046. 10.3389/fmed.2023.1151046 Raveendran AV, Jayadevan R, Sashidharan S, Long COVID (2021) An overview. Diabetes Metab Syndr 15(3):869–875. 10.1016/j.dsx.2021.04.007 Russo A, Biselli-Chicote PM, Kawasaki-Oyama RS et al (2018) Differential Expression of Prostaglandin I2 Synthase Associated with Arachidonic Acid Pathway in the Oral Squamous Cell Carcinoma. J Oncol 2018:6301980. 10.1155/2018/6301980 Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. 10.1101/gr.1239303 Shi Y, Andhey PS, Ising C et al (2021) Overexpressing low-density lipoprotein receptor reduces tau-associated neurodegeneration in relation to apoE-linked mechanisms. Neuron. ;109(15):2413–2426 e2417; 10.1016/j.neuron.2021.05.034 Sonnweber T, Pizzini A, Nairz M et al (2018) Arachidonic Acid Metabolites in Cardiovascular and Metabolic Diseases. Int J Mol Sci 19(11). 10.3390/ijms19113285 Szklarczyk D, Gable AL, Nastou KC et al (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49(D1):D605–D612. 10.1093/nar/gkaa1074 Taube S, Jiang M, Wobus CE (2010) Glycosphingolipids as receptors for non-enveloped viruses. Viruses 2(4):1011–1049. 10.3390/v2041011 Trevino TN, Fogel AB, Minshall R et al (2023) Caveolin-1 mediates neuroinflammation and cognitive impairment in SARS-CoV-2 infection. bioRxiv. 10.1101/2023.10.18.563024 Trivedi UH, Cezard T, Bridgett S et al (2014) Quality control of next-generation sequencing data without a reference. Front Genet 5:111. 10.3389/fgene.2014.00111 Trushina E, Du Charme J, Parisi J et al (2006) Neurological abnormalities in caveolin-1 knock out mice. Behav Brain Res 172(1):24–32. 10.1016/j.bbr.2006.04.024 Varughese JT, Buchanan SK, Pitt AS (2021) The Role of Voltage-Dependent Anion Channel in Mitochondrial Dysfunction and Human Disease. Cells 10(7). 10.3390/cells10071737 Wang H, Yang P, Liu K et al (2008) SARS coronavirus entry into host cells through a novel clathrin- and caveolae-independent endocytic pathway. Cell Res 18(2):290–301. 10.1038/cr.2008.15 Wong JP, Damania B (2021) SARS-CoV-2 dependence on host pathways. Science 371(6532):884–885. 10.1126/science.abg6837 Wu C, Liu Y, Yang Y et al (2020) Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B 10(5):766–788. 10.1016/j.apsb.2020.02.008 Wu P, Chen D, Ding W et al (2021) The trans-omics landscape of COVID-19. Nat Commun 12(1):4543. 10.1038/s41467-021-24482-1 Xu K, Nagy PD (2015) RNA virus replication depends on enrichment of phosphatidylethanolamine at replication sites in subcellular membranes. Proc Natl Acad Sci U S A 112(14):E1782–1791. 10.1073/pnas.1418971112 Yang LG, March ZM, Stephenson RA et al (2023) Apolipoprotein E in lipid metabolism and neurodegenerative disease. Trends Endocrinol Metab 34(8):430–445. 10.1016/j.tem.2023.05.002 Yong SJ (2021) Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infect Dis (Lond) 53(10):737–754. 10.1080/23744235.2021.1924397 Zawilska JB, Kuczynska K (2022) Psychiatric and neurological complications of long COVID. J Psychiatr Res 156:349–360. 10.1016/j.jpsychires.2022.10.045 Zhang H, Shao L, Lin Z et al (2022) APOE interacts with ACE2 inhibiting SARS-CoV-2 cellular entry and inflammation in COVID-19 patients. Signal Transduct Target Ther 7(1):261. 10.1038/s41392-022-01118-4 Zhong J, Sharma J, Raju R et al (2014) TSLP signaling pathway map: a platform for analysis of TSLP-mediated signaling. Database (Oxford) 2014:bau007. 10.1093/database/bau007 Tables Table 1: List of hub nodes in the protein–protein interaction networks Hub nodes Degree centrality Number of interacting ion channels Number of interacting lipid metabolism- related genes PRKACA 72 36 36 PRKACB 71 36 35 PRKACG 69 35 34 CYP2E1 61 0 61 LPL 58 0 58 AKT1 56 2 56 PPARG 55 0 55 INS 54 5 49 CACNA1C 53 42 11 APOE 44 1 43 CYP1A1 43 0 43 CYP7A1 42 0 42 FASN 42 0 42 PLPP2 42 0 42 PLPP3 42 0 42 PTGS2 42 0 42 MED1 42 0 42 APOB 41 0 41 DGAT1 41 0 41 PTEN 41 0 41 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryFigureS1DEGperPW.tif SupplementaryFigureS2nspPart1.jpg Supplementarytable1HICLMGStringPPIN.tsv SupplementaryTable2STRINGpathways.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5224427","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363590856,"identity":"297e44be-f59f-4fae-8775-dfb8719c6992","order_by":0,"name":"John Philip George","email":"","orcid":"","institution":"Institute of Bioinformatics, International Technology Park, Bangalore 560066, India","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Philip","lastName":"George","suffix":""},{"id":363593410,"identity":"2ccfacc4-c8e7-4df3-99cc-121f0e78b1ab","order_by":1,"name":"K. T. 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interaction with lipid metabolism-related genes in SARS-CoV-2 infected patients.\u003c/p\u003e","description":"","filename":"Figure1Workflow.png","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/f06981e1044fb6fdecd6b649.png"},{"id":66326501,"identity":"e963b979-707c-43e1-9f86-8c83f3ec8c01","added_by":"auto","created_at":"2024-10-10 12:56:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":400068,"visible":true,"origin":"","legend":"\u003cp\u003eGene set enrichment analysis showing the top 10 enriched components along with their percentages as pie charts for (A) biological processes, (B) cellular components, and (C) molecular function.\u003c/p\u003e","description":"","filename":"Figure2GSEA.png","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/21390a18d8d6e8980776381f.png"},{"id":66327033,"identity":"0ab5375d-d274-4dc5-8a51-b2027f567fab","added_by":"auto","created_at":"2024-10-10 13:04:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1161156,"visible":true,"origin":"","legend":"\u003cp\u003eDepiction of protein-protein interaction networks (PPINs) A: PPINs between human ion channels (HICs) (blue), lipid metabolism-related proteins (yellow), and SARS-CoV-2 proteins (Red). From the top 20 selected hub nodes (Right side), 1 was of HICs and 19 were of lipid metabolism-related genes. Out of the 19-lipid metabolism-related protein hub nodes, 6 interacted with the HIC. B represent PPINs between HICs, lipid metabolism-related proteins and SARS-CoV-2 proteins (S, N, M, E).\u003c/p\u003e","description":"","filename":"Figure3ppi100824.png","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/0dc89badbf8186d5387d2469.png"},{"id":66326503,"identity":"c96fb74e-b118-4580-aa5e-19ee2898d4fb","added_by":"auto","created_at":"2024-10-10 12:56:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2998486,"visible":true,"origin":"","legend":"\u003cp\u003ePathway map represents the alteration in the calcium signaling, insulin secretion, long-term depression, cholesterol metabolism, inflammatory mediator regulation of TRP channels, taste transduction, and type 2 diabetes and the corresponding most common phenotypes observed in the SARS-CoV-2-infected patients due to the deregulated HICs and lipid metabolism-related protein.\u003c/p\u003e","description":"","filename":"Figure4PathwayMap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/1d99fd763bb775bc0daa1588.jpg"},{"id":66329611,"identity":"c9db0390-ee8e-452e-a3e3-4b0cb7e1d8da","added_by":"auto","created_at":"2024-10-10 13:20:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5381149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/efd13757-83f5-4dd2-bbcf-36be5879b971.pdf"},{"id":66326508,"identity":"e75aa40e-9302-47e7-9ded-2fdb1169d268","added_by":"auto","created_at":"2024-10-10 12:56:13","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22775828,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1DEGperPW.tif","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/27c13e315c1b9a6223799fa1.tif"},{"id":66326507,"identity":"908271c0-c7ff-4a5a-afcd-aaa87764fb21","added_by":"auto","created_at":"2024-10-10 12:56:13","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3149313,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2nspPart1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/baa12b0ec41490250018f189.jpg"},{"id":66326502,"identity":"18e7850d-faae-4823-b150-e2af60b4528d","added_by":"auto","created_at":"2024-10-10 12:56:13","extension":"tsv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":561683,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1HICLMGStringPPIN.tsv","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/5cc43ef79c64f69634cd756a.tsv"},{"id":66329342,"identity":"08a30e8c-7267-4736-941f-5830359a5bea","added_by":"auto","created_at":"2024-10-10 13:12:13","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21335,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2STRINGpathways.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5224427/v1/e579155ed38f1c65a2999b3e.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentifying the interactome of altered ion channels with lipid metabolism in SARS-CoV-2 infected patients in post-COVID-19 era\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe SARS-CoV-2 is a positive-sense single-stranded RNA virus with a faster rate of mutation (Markov et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Various strains of the virus continued from December 2019 to December 2022, sequentially, beginning with the pre-variant of concern and progressing to the variation of concern, which primarily included Alpha, Delta, BA.1, BA.2, BA.5, and BQ.1. The drop in mortality rate lessened the catastrophic stress due to COVID-19 pandemic, however, the post-infectious conditions like long COVID results into new apprehensions. Individuals who were infected with the SARS-CoV-2 virus are at risk of acquiring long COVID (Marjenberg et al., Yong, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Marjenberg et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs defined by the National Academies of Sciences, Engineering and Medicine, long COVID is an infection-associated chronic condition (IACC) that occurs after a COVID-19 infection that is present for at least 3 months as a continuous relapsing and remitting, or progressive disease state that affects one or more organ systems (MedicalNewsToday, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The symptoms that are observed in the post-COVID scenario include fatigue; palpitations; neurological symptoms such as brain fog, insomnia, change in smell or taste, depression, or anxiety; digestive symptoms including diarrhea and stomach pain and other symptoms including irregularity in menstrual cycle, joint or muscle pain (Raveendran et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Yong, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This could result from the interaction of viral proteins with other host systemic proteins. A meta-analysis that includes the understanding of viral-host protein may contribute significant insights that could be considered further for the better management of the current condition of long COVID where direct diagnosis and pathophysiology are not known (Davis et al.).SARS-CoV-2 controls the host\u0026rsquo;s translation machinery for the translating its proteins (Wong and Damania, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Hoenen and Groseth, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Right from its entry into the cell, it engages with several host proteins like ACE2, TMPRSS2, and cathepsin along with some other membrane proteins (Jackson et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, viruses developed multiple mechanisms for exploiting normal host cell functions (Hoenen and Groseth, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One of the most crucial virus-host interactions is the modulation of host ion channel activity by viral proteins (Navarese et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Ion channels are transmembrane proteins that allow ions to flow passively across the cellular membranes due to their electrochemical gradient. This ion exchange across the membrane generates electrical currents that play a variety of roles, including the generation of membrane potential and cellular activities such as signal transduction, synaptic neurotransmitter release, and apoptosis (Kim, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eViruses exploit the cellular environment and replicate inside a host that by relying on the flow of ions into and out of the cell (Gordon et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Particularly, viruses of small-sized genomes rely on the host genomic machinery for many of the vital functions that they perform by interacting with the membrane proteins (Kim, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). SARS-CoV-2 utilizes HICs for the release of Ca\u003csup\u003e2+\u003c/sup\u003e ions required for the conformational changes in viral S-protein before entering endosomes and fusion with lysosomal membranes (Li, 2016), (Navarese et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Perlman and Netland, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This increases cytosolic calcium concentration and promotes viral replication by inhibiting host protein trafficking and viral protein maturation (Jayaseelan and Paramasivam, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Wang et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), (Glebov, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previously, we studied the interaction of HICs with SARS-CoV-2 proteins (Munjal et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). HICs including TRPM4, KCNN4, TRPA1, and GJA1 were observed to play roles in several pathways such as insulin secretion, and gap junctions, among others (Munjal et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSignificant alterations occur in metabolic regulation including lipids and lipoproteins during bacterial, viral, and parasitic infections (Filippas-Ntekouan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The role of lipids in viral infection includes membrane fusion, replication, endocytosis, and exocytosis (Abu-Farha et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It has been found that the patients infected with SARS-CoV-2 have lower levels of cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, and apolipoprotein B and A-I, similar to other infections (Feingold, 2000), (Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). With recovery from SARS-CoV-2 infections, the serum lipid levels return to normal (Feingold, 2000). Coronavirus first seizes the intracellular membranes of the host cells to form new compartments known as double-membrane vesicles (DMVs) that are required for viral genome amplification. Viruses require specific phospholipid composition to form the ideal replicative organelles for their replication (Xu and Nagy, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). DMVs are membranous structures containing viral proteins and an array of captured host factors that facilitate an exclusive lipid micro-environment suitable for the replication of coronavirus inside the host (Knoops et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). To modify host cells and produce lipids for viral envelopes, viruses attack lipid synthesis and signaling (Murillo et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, lipid biogenesis pathways are affected by receptor-mediated virus entry at the endosomal cell surface (Taube et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), (Chazal and Gerlier, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther, interactions between HICs and lipid metabolism-related proteins result in changes in cellular lipid composition and alterations in various signaling pathways (Maus et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Parthasarathi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides, lipids affect channel gating through direct lipid-protein interactions (Cordero-Morales and Vasquez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ion channel blockers like Tetrandrine have potentially shown antiviral properties by inhibiting viral replication through the endolysosomal pathway (Grimm and Tang, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Heister and Poston, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In similar ways, identifying the interactions between deregulated HICs and lipid metabolism-related proteins would thus aid in understanding the key pathways resulting in dynamic changes observed (Li et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the lipid metabolism of patients with history of SARS-CoV-2 infection. This would result in identifying HICs that could act as potential biomarkers in managing post COVID condition in SARS-CoV-2 infected petients (Wu et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, the transcriptomic profiles of 277 patients infected with SARS-CoV-2 and 76 control samples were analyzed and the deregulated HICs and lipid metabolism-related genes in SARS-CoV-2 infection were identified. Protein-protein interaction (PPI) networks were generated to exhibit the interactions between the deregulated HICs and lipid metabolism-related proteins. Furthermore, the PPIs identified between SARS-CoV-2 proteins with the deregulated HICs and lipid metabolism-related proteins indicated a plausible interdependence between them. A pathway map was generated to depict the altered pathways including calcium signaling, long-term depression, and cholesterol metabolism as a result of deregulated proteins. Furthermore, drugs known to activate, inhibit, block, or modulate potential HICs were identified. In the future, experimental studies on these interactions would aid in attaining many more mechanistic insights into the SARS-CoV-2 infection.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003eThe overall workflow for the identification of potential HICs and their interaction with lipid metabolism-related genes in SARS-CoV-2 infected patients is depicted in Figure 1.\u003c/p\u003e\n\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Data collection\u003c/h2\u003e\n\u003cp\u003eA list of 376 HICs was downloaded from the Human Genome Organization Gene Nomenclature Committee database and 1087 lipid metabolism-related genes were obtained from the literature\u0026nbsp;(Li et al., 2020),\u0026nbsp;(Fernandez et al., 2020),\u0026nbsp;(Dunn et al., 2019),\u0026nbsp;(Moskot et al., 2015),\u0026nbsp;(Meng et al., 2021),\u0026nbsp;(El-Fasakhany et al., 2001),\u0026nbsp;(Russo et al., 2018),\u0026nbsp;(Lutz and Cornett, 2013),(Astudillo et al., 2012),(Sonnweber et al., 2018),(Hanna and Hafez, 2018). RNA sequencing (RNA-seq) paired-end data was downloaded from the National Center for Biotechnology Information-Sequence Read Archive (GSE157103) and The European Bioinformatics Institute-The European Nucleotide Archive (ERP127339) for 277 patients infected with SARS-CoV-2\u0026nbsp;(Overmyer et al., 2021),\u0026nbsp;(Wu et al., 2021). 76 control samples were obtained from the Genotype-Tissue Expressions (GTEx) portal and GSE157103 dataset.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Data processing\u003c/h2\u003e\n\u003cp\u003eThe quality of the reads was checked using FastQC (v0.11.9)\u0026nbsp;(Trivedi et al., 2014), and the good quality reads were aligned to the human reference genome GRCh38.p13 using STAR aligner (v2.7.10a)\u0026nbsp;(Dobin et al., 2013). Gene expression quantification was performed using HTSeq (v1.99.2)\u0026nbsp;(Anders et al., 2015)\u0026nbsp;for each sample. Further, the expression profiles corresponding to HICs and lipid metabolism-related genes were parsed.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Identification of differentially expressed genes\u003c/h2\u003e\n\u003cp\u003eThe gene-wise read count data for HICs and lipid metabolism-related genes were used as inputs for DESeq-2\u0026nbsp;(Love et al., 2014), an R Bioconductor (v4.2.1) package for obtaining differentially expressed genes (DEGs). The samples from the study GSE157103 were treated as technical replicates using collapseReplicates\u0026nbsp;(Love et al., 2014). The parameters used for the identification of DEGs were a log2-fold change of |0.6| and Benjamini Hochberg-adjusted p values \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Enrichment Analysis for Functional Clustering of DEGs\u003c/h2\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) was used to determine the key biological processes, cellular components, and molecular functions using Enrichr\u0026nbsp;(Chen et al., 2013)\u0026nbsp;for the DEGs. Biological annotations with significant adjusted P-values were selected. Further, extensive data mining was performed to identify potential molecules pertaining to SARS-CoV-2 infection.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Protein-protein interaction networks\u0026nbsp;\u003c/h2\u003e\n\u003ch3\u003e2.5.1\u0026nbsp;\u0026nbsp;Altered HICs with lipid metabolism-related proteins\u003c/h3\u003e\n\u003cp\u003eProtein-protein interaction networks (PPINs) of the differentially expressed HICs and lipid metabolism-related proteins were constructed using the STRING (v12.0) database\u0026nbsp;(Szklarczyk et al., 2021). The interactions with a high confidence and false discovery rate of 1% were visualized in Cytoscape (v3.10.1)(Shannon et al., 2003)\u0026nbsp;and Gephi (v0.10)\u0026nbsp;(Amith et al., 2019). Further, the obtained network was analyzed using NetworkAnalyser, a Cytoscape plugin, and various properties of the network including degree centrality and betweenness centrality were estimated. The top 20 nodes with the highest degree centrality measure were selected as hub nodes in the network.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.5.2\u0026nbsp;\u0026nbsp;Critical subnetworks in PPINs using the hub nodes\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eHub nodes are the proteins with a higher degree centrality, which refers to several interactions between the respective node towards the other nodes/proteins in the network. Molecular Complex Detection (MCODE)\u0026nbsp;(Bader and Hogue, 2003), a Cytoscape plugin, identifies densely connected regions based on prior knowledge of hub nodes from the PPINs obtained from the STRING database. Highly interacting subnetworks from the complex PPINs could be obtained using this plugin. The parameters for maximum depth and k-score were set to 100 and 2, respectively. Similarly, the node score cutoff was set to 0.2. Further, the subnetworks with a minimum of 10 nodes including both HICs and lipid metabolism-related proteins were selected as significant subnetworks. Pathway enrichment analysis for the subnetworks that included both ion channels and lipid metabolism-related proteins was performed using a Cytoscape plugin, ClueGO (v2.5.10)\u0026nbsp;(Bindea et al., 2009).\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.5.3\u0026nbsp;\u0026nbsp;SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins\u003c/h3\u003e\n\u003cp\u003eThe binary interactions of SARS-CoV-2 proteins with deregulated human proteins were downloaded from the BioGRID (v4.4.215) database (updated on 31\u003csup\u003est\u003c/sup\u003e October 2022)\u0026nbsp;(Oughtred et al., 2021). The interactions corresponding to HICs and lipid metabolism-related proteins with SARS-CoV-2 proteins were parsed from the list of interactions. Intra-protein interactions of the deregulated HICs and lipid metabolism-related genes interacting with each SARS-CoV-2 protein were generated using the STRING (v12.0) database\u0026nbsp;(Szklarczyk et al., 2021). Thereafter, the interactions were overlaid with SARS-CoV-2 proteins and were visualized in Cytoscape (v3.10)\u0026nbsp;(Shannon et al., 2003). The interactions of SARS-CoV-2 proteins with both deregulated HICs and lipid metabolism-related genes were extracted.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.6\u0026nbsp; \u0026nbsp; \u0026nbsp;Generation of pathway map of HICs and lipid metabolism-related proteins\u003c/h2\u003e\n\u003cp\u003eThe pathways involving the deregulated HICs and lipid metabolism-related proteins were determined using the STRING database. Following that, a comprehensive data mining approach was used to screen studies from the PubMed database referring to changes detected in pathways involving altered HICs and lipid metabolism-related proteins in SARS-CoV-2-infected patients. The interactions between the molecules in pathways including transportation, translocations, inhibitions, and activations were annotated\u0026nbsp;(Zhong et al., 2014),\u0026nbsp;(Parthasarathi et al., 2021). Thereafter, a pathway map was generated using PathVisio (v3.3.0)\u0026nbsp;(Kutmon et al., 2015)\u0026nbsp;in Graphical Pathway Markup Language format. It included pathways that could lead to the most common symptoms observed in SARS-CoV-2-infected patients involving deregulated HICs and lipid metabolism-related proteins.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.7\u0026nbsp; \u0026nbsp; \u0026nbsp;Identification of drugs interacting with HICs\u003c/h2\u003e\n\u003cp\u003eThe list of drugs interacting with differentially expressed HICs that interacted with both lipid metabolism-related proteins and SARS-CoV-2 proteins was parsed from the Drug Gene Interaction database (DGIdb) (Freshour et al., 2021). \u0026nbsp;The drugs with a known mode of interaction with the HICs were depicted using a customized Python script.\u0026nbsp;\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Differentially expressed HICs and Lipid metabolism-related genes in SARS-CoV-2-infected patients\u003c/h2\u003e\n\u003cp\u003eA list of 287 differentially expressed HICs was identified,\u0026nbsp;out of which,\u0026nbsp;218 genes were found to be up-regulated and 69 to be down-regulated. Similarly, 754 differentially expressed lipid metabolism-related genes were identified, of which, 509 genes were up-regulated and 245 were down-regulated in SARS-CoV-2-infected patients.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Functional clustering of DEGs\u003c/h2\u003e\n\u003cp\u003ePathologically significant annotations for groups of deregulated HICs and lipid metabolism-related genes were identified in terms of biological processes, cellular components, and molecular functions that were reported to be altered in SARS-CoV-2 patients. Most gene groups were identified as being enriched with a higher combined score in cholesterol transport and binding; response to salt; neurotransmitter receptor complex, neuron projection, and chemical synaptic transmission; lipid transportation; Ca\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, channel activity; gap junction assembly and channel activity (Figure 2 and Supplementary Figure S1)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Densely interconnected subnetworks through PPINs\u003c/h2\u003e\n\u003cp\u003ePPINs\u0026nbsp;determined through the STRING database of the differentially expressed HICs and lipid metabolism-related proteins revealed the predicted protein-protein interactions between them\u0026nbsp;(Figure 3A, Supplementary Table S1).\u0026nbsp;Further, the network analyses of the PPINs using NetworkAnalyser\u0026nbsp;(Assenov et al., 2008)\u0026nbsp;resulted in the identification of the hub nodes in the network (Table 1). Hub nodes in a network are highly interconnected nodes. Out of the top 20 selected hub nodes in the network, 1 was HIC and 19 were\u0026nbsp;lipid metabolism-related proteins. 6 of the 19-hub lipid metabolism-related proteins – PRKACA, PRKACB, PRKACG, AKT1, INS, and APOE interacted with HICs (Table 1). Apolipoprotein E (APOE), an important lipid metabolism-related protein, shows a strong association with many neurodegenerative diseases\u0026nbsp;(Shi et al., 2021),\u0026nbsp;(Yang et al., 2023)\u0026nbsp;along with SARS-CoV-2 infection, especially in regards to post-COVID. Interaction between APOE and ACE2 has been reported to block the cellular entry of SARS-CoV-2 while reducing inflammation in patients\u0026nbsp;(Zhang et al., 2022). Additionally, the deregulated APOE was found to be enriched in cholesterol metabolism using GSEA\u0026nbsp;(Zhang et al., 2022),\u0026nbsp;(Mahley, 2016).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLikewise, deregulated CACNA1C was found to be interacting with 11\u0026nbsp;lipid metabolism-related proteins. 47 highly interconnecting subnetworks were obtained by using the MCODE (Supplementary Table S3). 6 subnetworks that included HICs and lipid metabolism-related proteins were filtered out as significant subnetworks (Supplementary Figure S2). Pathway enrichment analysis for these subnetworks using\u0026nbsp;ClueGO resulted in the identification of gap junction, inflammatory mediator regulation of TRP channels, insulin secretion, insulin resistance, taste transduction, cholesterol metabolism, long-term depression, type II diabetes mellitus (Supplementary Figure S2, Supplementary Table S4). Furthermore,\u0026nbsp;pathway enrichment through the STRING database resulted in the identification of 176 pathways out of which 53 pathways included the deregulated HICs and lipid metabolism-related proteins (Supplementary Table S1, S2, and Supplementary Figure S1) and were found to be associated with SARS-CoV-2 infection utilizing extensive data mining.\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;PPINs of SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eA list of 28,250 binary interactions between SARS-CoV-2 proteins and human proteins was obtained from the BioGRID database. Of these, 37 deregulated HICs interacted with 23 SARS-CoV-2 proteins resulting in 190 unique interactions between them, and 281 deregulated lipid metabolism-related proteins interacted with 30 SARS-CoV-2 proteins resulting in 1218 unique interactions. 23 SARS-CoV-2 proteins: \u0026nbsp;E, M, S, Nsp1, Nsp2, Nsp3, Nsp4, Nsp5, Nsp6, Nsp8, Nsp9, Nsp13, Nsp14, Nsp15, Orf3a, Orf3b, Orf6, Orf7a, Orf7b, Orf8, Orf9c, Orf10 and Orf14 interacted with both deregulated HICs and lipid metabolism-related proteins (Supplementary Table S5). The structural protein S interacted with 6 deregulated HICs and 67 lipid metabolism-related proteins (Figure 3B) (Supplementary Figure S3 to S6).\u003c/p\u003e\n\u003ch2\u003e3.5\u0026nbsp; \u0026nbsp; \u0026nbsp;Pathway map of altered HICs and their interactions with lipid metabolism-related proteins\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp;A pathway map was constructed using the STRING database and CLueGO for PPINS and subnetworks, to visually represent the pathologically significant altered pathways in patients. Figure 4 depicts the roles of deregulated HICs and lipid metabolism-related proteins in pathways contributing to the loss of taste, dysregulation in long-term depression, insulin secretion, airway inflammation, and pulmonary fibrosis. \u0026nbsp;The pathway map consists of 169 molecules and 232 reactions.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.6\u0026nbsp; \u0026nbsp; \u0026nbsp;Drugs interacting with HICs:\u003c/h2\u003e\n\u003cp\u003eOut of a total of 376 HICs, 4 HICs namely, GJA1, ITPR2, ITPR3, and KCNB2 interacted with lipid metabolism-related and SARS-CoV-2 proteins; were identified to interact with 17 drugs including quinine, octanol, flufenamic acid, tetraethylammonium, and dalfampridine. These drugs are known to interact with these HICs with known modes of action which range from being activator, antagonist, blocker, or inhibitor (Supplementary figure 6).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eSARS-CoV-2 remains an endemic virus, that can potentially infect, hospitalize, and even kill people. The mortality rate from the SARS-CoV-2 virus infection appeared to drop in early 2023, however, the repercussions on afflicted people continue to worry the scientific community. These effects are no longer limited to the respiratory system, but have spread to other systems of the human body, generating a variety of symptoms such as excessive weariness, brain fog, irregular menstrual cycle, fever, and impaired mobility. Such persistence or development of new symptoms in individuals infected with SARS-CoV-2 is usually referred to as post-COVID conditions. A critical molecular investigation of the SARS-CoV-2 infected patients stands to be a promising approach to gain insights into the molecular changes taking place in the patients with a history of SARS-CoV-2 infection.\u003c/p\u003e \u003cp\u003eThe consequences of viral exploitation on the host transcriptome were investigated, and changes in the transcriptome of SARS-CoV-2 infected patients were studied. This revealed differences in the expression of several HICs and lipid metabolism-related genes in patients. Pathologically significant annotated groups of altered genes that may cause pathology in patients were found to be enriched using GSEA. Annotations - in terms of biological processes, cellular components, and molecular functions that highlighted ion channels transport and activity; cholesterol transport and binding; insulin response, gap junction assembly, and activity; lipid transportation and activation were obtained through this study.\u003c/p\u003e \u003cp\u003eFurther, PPINs depicted the interactions between the HICs and lipid metabolism-related proteins and led to identification of pathways. Interesting findings in the form of overlapping pathways depicted the deregulation of pathways such as insulin secretion, calcium signaling, gap junction, cholesterol metabolism, inflammatory mediator regulation of TRP channels, long-term depression, gap junction, renin secretion, and apelin signaling, type II diabetes mellitus, taste transduction, GABAergic synapse, serotonergic synapse, cAMP signaling pathway, RAS signaling pathway, cortisol synthesis and secretion, pancreatic secretion, thyroid hormone synthesis, glucagon signaling pathway and other pathologically critical pathway in SARS-CoV-2 infection. Moreover, these findings correlated with our previous study (Munjal et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, a pathway map consisting of the pathways including long-term depression, cholesterol metabolism, and type II diabetes mellitus was generated. In addition, calcium signaling, inflammatory mediator regulation of TRP channels, insulin secretion, and taste transduction were also represented in the pathway map. Here, we highlight the pathophysiology of following pathways:\u003c/p\u003e \u003cp\u003e\u0026bull; \u003cb\u003eLong-term depression\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePersistence or development of new symptoms in patients infected with SARS-CoV-2 infection referred to as post-COVID affects various organ systems including the central and peripheral nervous system (Zawilska and Kuczynska, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several patients also experience sleep disorders and depression with other nervous breakdowns (Zawilska and Kuczynska, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), (Olgun Yildizeli et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). HICs including the InsP3 receptor family, and phospholipase A2, among others (Supplementary Table S2) were identified to be deregulated in patients. HICs play a critical role by connecting the inferior olivary neurons in the medulla oblongata through gap junctions (Leznik and Llinas, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) (also identified to be deregulated as a pathway), maintaining their membrane potential (Llinas and Yarom, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). A decrease in the diffusion of AMPAR, a ligand-gated ion channel, at the synapses of the neurons could be a result of a rise in the intracellular calcium, which is again a central element in the synaptic plasticity (Henley and Wilkinson, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), (Anggono and Huganir, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Our study has identified these ion channels namely CACNA1A, and ITPRs which are responsible for maintaining the intracellular calcium levels, to be deregulated.\u003c/p\u003e \u003cp\u003e\u0026bull; \u003cb\u003eCholesterol metabolism\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAlterations in HICs are known to contribute in the deregulation of cholesterol metabolism (Cure and Cumhur Cure, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Maxfield and Wustner, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), (Craig et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cholesterol-enriched lipid rafts serve as a platform for entry of SARS-CoV-2 by endocytosis (Palacios-Rapalo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is known to recruit receptors like ACE2, and transmembrane serine protease 2 for their interaction with viral spike-protein (Palacios-Rapalo et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), describing cholesterol levels are crucial for viral penetration (Kowalska et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lower levels of high-density lipoprotein cholesterol (HDL-C) are associated with increased vulnerability to SARS-CoV-2 infection along with other infections, whereas elevated HDL-C levels are linked to reduced susceptibility to these conditions (Kocar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kowalska et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVoltage-dependent anion channels (VDACs), are located on the mitochondrial membrane and facilitate the transfer of metabolites and ions in the mitochondria (Varughese et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), (Campbell and Chan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Its alteration results in the alteration of cholesterol synthesis and transport (Varughese et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). VDACs, apolipoproteins, among other molecules (Supplementary Table S2) were found to be deregulated in the patients.\u003c/p\u003e \u003cp\u003eThe pathway map highlights the alteration in pathophysiologically critical pathways in the context of SARS-CoV-2 infection and the role of deregulated HICs and lipid metabolism-related proteins in it. Some of these pathways were also identified in our previous study (Munjal et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlighting the importance of network-based approaches.\u003c/p\u003e \u003cp\u003e4 out of 376 HICs, that interact with lipid metabolism and SARS-CoV-2 proteins, were identified to interact with 17 drug molecules using DGIdb with a known mode of action.\u003c/p\u003e \u003cp\u003eGap Junction Protein Alpha 1 (GJA1) was identified to interact with Caveolin-1 (CAV1), a lipid metabolism-related protein, and seven SARS-CoV-2 proteins (M, nsp4, nsp6, ORF14, ORF3b, ORF7a, ORF7b). CAV1, which is an integral membrane protein, is critically involved in regulating the blood-brain barrier (Huang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several studies report the upregulation of CAV1 in the forebrain of SARS-CoV-2-infected patients (Green et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), (Premkumar and Sajitha Lulu, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Upregulation of CAV1 further results in increased expression of vascular cell adhesion molecule-1 (VCAM-1) and CD3\u003csup\u003e+\u003c/sup\u003e T cell infiltration of the hippocampus, a region responsible for memory and short-term learning. This cascading upregulation of proteins was observed in SARS-CoV-2 infection, which contributed to neuroinflammatory symptoms including deficiency in learning and memory (Trevino et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Knockout studies on mice with CAV1 deficiency showed protection against neuroinflammatory symptoms during SARS-CoV-2 infection (Gioiosa et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), (Trushina et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). CAV1 which interacted with GJA1, was also found to be upregulated in SARS-CoV-2-infected patients. Langlois and colleagues reported the direct binding and interdependence between GJA1 and CAV1 (Langlois et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Moreover, potential drug molecules like Carbenoxolone, Octanol, and Flufenamic acid were found to interact with GJA1 (Fig.\u0026nbsp;7). Most likely, GJAI could be one of the potential target molecules for drug repurposing.\u003c/p\u003e \u003cp\u003eLikewise, we identified drugs interacting with other three significantly deregulated HICs namely, Inositol 1,4,5-Trisphosphate Receptor Type 2 (ITPR2), Inositol 1,4,5-Trisphosphate Receptor Type 2 (ITPR3), and Potassium Voltage-Gated Channel Subfamily B Member 2 (KCNB2) (Supplementary Table S6 and S7) identified to be interacting with lipid metabolism-related and SARS-CoV-2 proteins.\u003c/p\u003e \u003cp\u003eSimilarly, drugs that interact with HICs with known functions could be studied further to understand their physiological and therapeutic roles in SARS-CoV-2.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, a transcriptomic analysis of 277 SARS-CoV-2 infected patients revealed alteration of 1041 differentially expressed HICs and lipid metabolism-related genes in the patients. GSEA in combination with strong protein-protein interactions between these groups of proteins led to the enrichment of pathologically critical biological processes and pathways like \u0026ndash; cholesterol metabolism, long-term depression, and taste transduction, among others. Further, we identified 17 drugs interacting with 4 potential HICs (\u003cem\u003eGJA1\u003c/em\u003e, \u003cem\u003eITPR2\u003c/em\u003e, \u003cem\u003eITPR3\u003c/em\u003e, and \u003cem\u003eKCNB2\u003c/em\u003e) that interact with lipid metabolism and SARS-CoV-2 proteins. Targeting them, with further experimental validations, by the means of drug repurposing could aid in better management of the disease condition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the authors of the manuscripts for making the datasets used in this study publicly available.\u0026nbsp;J.S. is thankful for funding from the ICMR, Government of India (ICMR Ref No. BMI/12(116)2021 and ICMR Ref No. BMI/12(95)/2021)\u0026nbsp;and Council of Scientific and Industrial Research (CSIR), Government of India [37WS(0114)/2023-24/EMR-II/ASPIRE].. K. T. S. P. is supported by ICMR, Government of India (ICMR Ref No. BMI/12(95)2021), J. P. G. is supported by ICMR, Government of India (ICMR Ref No. BMI/12(116)/2021), K. B. G. is supported by the ICMR, Government of India (ICMR Ref No. BMI/Adhoc/28/2022-23). This work was also supported by a grant from DBT/Wellcome Trust India Alliance entitled \u0026ldquo;Center for Rare Disease Diagnosis, Research, and Training\u0026rdquo; (IA/CRC/20/1/600002) to A.P.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJohn Philip George:\u003c/strong\u003e Methodology, Investigation, Data curation, Visualization, Writing- Original draft preparation.\u0026nbsp;\u003cstrong\u003eK. T. Shreya Parthasarathi:\u003c/strong\u003e Methodology, Visualization, Investigation.\u0026nbsp;\u003cstrong\u003eKiran Bharat Gaikwad:\u003c/strong\u003e Writing- Reviewing and Editing.\u0026nbsp;\u003cstrong\u003eShweta Rana:\u003c/strong\u003e Writing- Reviewing and Editing.\u0026nbsp;\u003cstrong\u003eVibha Gupta:\u003c/strong\u003e Resources, Supervision.\u0026nbsp;\u003cstrong\u003ePunit Kaur\u003c/strong\u003e: Writing- Reviewing and Editing.\u0026nbsp;\u003cstrong\u003eAkhilesh Pandey:\u003c/strong\u003e Supervision, Writing- Reviewing and Editing, Funding acquisition.\u0026nbsp;\u003cstrong\u003eHarpreet Singh:\u003c/strong\u003e Supervision, Writing- Reviewing and Editing. \u0026nbsp;\u003cstrong\u003eJyoti Sharma:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Writing- Reviewing and Editing, Funding acquisition, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor(s) disclosure (Conflict of interest) statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Indian Council of Medical Research (ICMR), Government of India (ICMR Ref No. BMI/12(116)/2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. The data can be accessed using the following resources: NCBI SRA (https://www.ncbi.nlm.nih.gov/sra/?term=), ENA (https://www.ebi.ac.uk/ena/browser/home), GTEx portal (https://gtexporatal.org/home/datasets/) accessed on 2 August 2022. Customized R scripts used for identification of differentially expressed genes and COVID-19 ICs-LMG Pathway reaction data are available in the GPML format through the GitHub repository via the following URLs: (https://github.com/js-iob/COVID-19_ICs_LMG/blob/main/Differential_expression_RNA-Seq.R, https://github.com/js-iob/COVID-19_ICs_LMG/blob/main/COVID-19_ICs_LMG_PathwayMap.gpml) accessed on 3 June 2023.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbu-Farha M, Thanaraj TA, Qaddoumi MG et al (2020) The Role of Lipid Metabolism in COVID-19 Virus Infection and as a Drug Target. Int J Mol Sci 21(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms21103544\u003c/span\u003e\u003cspan address=\"10.3390/ijms21103544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmith MT, Fujimoto K, Tao C, NET-EXPO: (2019) A Gephi Plugin Towards Social Network Analysis of Network Exposure for Unipartite and Bipartite Graphs. HCI Int 2019 Posters 2019;1034:3\u0026ndash;12; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-030-23525-3_1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-23525-3_1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnders S, Pyl PT, Huber W (2015) HTSeq\u0026ndash;a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166\u0026ndash;169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btu638\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btu638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnggono V, Huganir RL (2012) Regulation of AMPA receptor trafficking and synaptic plasticity. Curr Opin Neurobiol 22(3):461\u0026ndash;469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.conb.2011.12.006\u003c/span\u003e\u003cspan address=\"10.1016/j.conb.2011.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssenov Y, Ramirez F, Schelhorn SE et al (2008) Computing topological parameters of biological networks. Bioinformatics 24(2):282\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btm554\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btm554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstudillo AM, Balgoma D, Balboa MA et al (2012) Dynamics of arachidonic acid mobilization by inflammatory cells. Biochim Biophys Acta 1821(2):249\u0026ndash;256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbalip.2011.11.006\u003c/span\u003e\u003cspan address=\"10.1016/j.bbalip.2011.11.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-4-2\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-4-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindea G, Mlecnik B, Hackl H et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25(8):1091\u0026ndash;1093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btp101\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btp101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell AM, Chan SH (2007) The voltage dependent anion channel affects mitochondrial cholesterol distribution and function. Arch Biochem Biophys 466(2):203\u0026ndash;210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.abb.2007.06.012\u003c/span\u003e\u003cspan address=\"10.1016/j.abb.2007.06.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChazal N, Gerlier D (2003) Virus entry, assembly, budding, and membrane rafts. Microbiol Mol Biol Rev 67(2):226\u0026ndash;237 table of contents. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/MMBR.67.2.226-237.2003\u003c/span\u003e\u003cspan address=\"10.1128/MMBR.67.2.226-237.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen EY, Tan CM, Kou Y et al (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-14-128\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-14-128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordero-Morales JF, Vasquez V (2018) How lipids contribute to ion channel function, a fat perspective on direct and indirect interactions. Curr Opin Struct Biol 51:92\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sbi.2018.03.015\u003c/span\u003e\u003cspan address=\"10.1016/j.sbi.2018.03.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCraig M, Yarrarapu SNS, Dimri M, Biochemistry (2023) Cholesterol. In: StatPearls. Treasure Island (FL)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCure E, Cumhur Cure M (2021) Strong relationship between cholesterol, low-density lipoprotein receptor, Na(+)/H(+) exchanger, and SARS-COV-2: this association may be the cause of death in the patient with COVID-19. Lipids Health Dis 20(1):179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-021-01607-5\u003c/span\u003e\u003cspan address=\"10.1186/s12944-021-01607-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis HE, McCorkell L, Vogel JM et al (2023) Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 21(3):133\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-022-00846-2\u003c/span\u003e\u003cspan address=\"10.1038/s41579-022-00846-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/bts635\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/bts635\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn TM, Tifft CJ, Proia RL (2019) A perilous path: the inborn errors of sphingolipid metabolism. J Lipid Res 60(3):475\u0026ndash;483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1194/jlr.S091827\u003c/span\u003e\u003cspan address=\"10.1194/jlr.S091827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl-Fasakhany FM, Uchimura K, Kannagi R et al (2001) A novel human Gal-3-O-sulfotransferase: molecular cloning, characterization, and its implications in biosynthesis of (SO(4)-3)Galbeta1-4(Fucalpha1-3)GlcNAc. J Biol Chem 276(29):26988\u0026ndash;26994. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M100348200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M100348200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeingold KR et al (2000) Lipid and Lipoprotein Levels in Patients with COVID-19 Infections. In: Endotext. (Feingold KR, Anawalt B, Boyce A, eds.) South Dartmouth (MA)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez LP, Gomez de Cedron M, Ramirez de Molina A (2020) Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment. Front Oncol 10:577420. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2020.577420\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.577420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilippas-Ntekouan S, Liberopoulos E, Elisaf M (2017) Lipid testing in infectious diseases: possible role in diagnosis and prognosis. Infection 45(5):575\u0026ndash;588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s15010-017-1022-3\u003c/span\u003e\u003cspan address=\"10.1007/s15010-017-1022-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreshour SL, Kiwala S, Cotto KC et al (2021) Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res 49(D1):D1144\u0026ndash;D1151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkaa1084\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGioiosa L, Raggi C, Ricceri L et al (2008) Altered emotionality, spatial memory and cholinergic function in caveolin-1 knock-out mice. Behav Brain Res 188(2):255\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbr.2007.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2007.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlebov OO (2020) Understanding SARS-CoV-2 endocytosis for COVID-19 drug repurposing. FEBS J 287(17):3664\u0026ndash;3671. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/febs.15369\u003c/span\u003e\u003cspan address=\"10.1111/febs.15369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon DE, Jang GM, Bouhaddou M et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583(7816):459\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-020-2286-9\u003c/span\u003e\u003cspan address=\"10.1038/s41586-020-2286-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen R, Mayilsamy K, McGill AR et al (2022) SARS-CoV-2 infection increases the gene expression profile for Alzheimer's disease risk. Mol Ther Methods Clin Dev 27:217\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.omtm.2022.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.omtm.2022.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm C, Tang R (2020) Could an endo-lysosomal ion channel be the Achilles heel of SARS-CoV2? Cell Calcium 88:102212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ceca.2020.102212\u003c/span\u003e\u003cspan address=\"10.1016/j.ceca.2020.102212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanna VS, Hafez EAA (2018) Synopsis of arachidonic acid metabolism: A review. J Adv Res 11:23\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jare.2018.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jare.2018.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeister PM, Poston RN (2020) Pharmacological hypothesis: TPC2 antagonist tetrandrine as a potential therapeutic agent for COVID-19. Pharmacol Res Perspect 8(5):e00653. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/prp2.653\u003c/span\u003e\u003cspan address=\"10.1002/prp2.653\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenley JM, Wilkinson KA (2013) AMPA receptor trafficking and the mechanisms underlying synaptic plasticity and cognitive aging. Dialogues Clin Neurosci 15(1):11\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.31887/DCNS.2013.15.1/jhenley\u003c/span\u003e\u003cspan address=\"10.31887/DCNS.2013.15.1/jhenley\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoenen T, Groseth A (2022) Virus-Host Cell Interact Cells 11(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells11050804\u003c/span\u003e\u003cspan address=\"10.3390/cells11050804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Q, Zhong W, Hu Z et al (2018) A review of the role of cav-1 in neuropathology and neural recovery after ischemic stroke. J Neuroinflammation 15(1):348. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12974-018-1387-y\u003c/span\u003e\u003cspan address=\"10.1186/s12974-018-1387-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson CB, Farzan M, Chen B et al (2022) Mechanisms of SARS-CoV-2 entry into cells. Nat Rev Mol Cell Biol 23(1):3\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41580-021-00418-x\u003c/span\u003e\u003cspan address=\"10.1038/s41580-021-00418-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayaseelan VP, Paramasivam A (2020) Repurposing calcium channel blockers as antiviral drugs. J Cell Commun Signal 14(4):467\u0026ndash;468. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12079-020-00579-y\u003c/span\u003e\u003cspan address=\"10.1007/s12079-020-00579-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JB, Channelopathies (2014) Korean J Pediatr 57(1):1\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3345/kjp.2014.57.1.1\u003c/span\u003e\u003cspan address=\"10.3345/kjp.2014.57.1.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnoops K, Kikkert M, Worm SH et al (2008) SARS-coronavirus replication is supported by a reticulovesicular network of modified endoplasmic reticulum. PLoS Biol 6(9):e226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pbio.0060226\u003c/span\u003e\u003cspan address=\"10.1371/journal.pbio.0060226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKocar E, Rezen T, Rozman D (2021) Cholesterol, lipoproteins, and COVID-19: Basic concepts and clinical applications. Biochim Biophys Acta Mol Cell Biol Lipids 1866(2):158849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbalip.2020.158849\u003c/span\u003e\u003cspan address=\"10.1016/j.bbalip.2020.158849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKowalska K, Sabatowska Z, Forycka J et al (2022) The Influence of SARS-CoV-2 Infection on Lipid Metabolism-The Potential Use of Lipid-Lowering Agents in COVID-19 Management. Biomedicines 10(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines10092320\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines10092320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutmon M, van Iersel MP, Bohler A et al (2015) PathVisio 3: an extendable pathway analysis toolbox. PLoS Comput Biol 11(2):e1004085. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pcbi.1004085\u003c/span\u003e\u003cspan address=\"10.1371/journal.pcbi.1004085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanglois S, Cowan KN, Shao Q et al (2008) Caveolin-1 and \u0026ndash;\u0026thinsp;2 interact with connexin43 and regulate gap junctional intercellular communication in keratinocytes. Mol Biol Cell 19(3):912\u0026ndash;928. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1091/mbc.e07-06-0596\u003c/span\u003e\u003cspan address=\"10.1091/mbc.e07-06-0596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeznik E, Llinas R (2005) Role of gap junctions in synchronized neuronal oscillations in the inferior olive. J Neurophysiol 94(4):2447\u0026ndash;2456. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/jn.00353.2005\u003c/span\u003e\u003cspan address=\"10.1152/jn.00353.2005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi F, Structure, Function, Evolution of Coronavirus Spike Proteins (2016) Annu Rev Virol 3(1):237\u0026ndash;261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-virology-110615-042301\u003c/span\u003e\u003cspan address=\"10.1146/annurev-virology-110615-042301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Li Q, Su Z et al (2020) Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma. Lipids Health Dis 19(1):222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-020-01390-9\u003c/span\u003e\u003cspan address=\"10.1186/s12944-020-01390-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang Y, Lu R et al (2021) Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta 517:66\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2021.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2021.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlinas R, Yarom Y (1986) Oscillatory properties of guinea-pig inferior olivary neurones and their pharmacological modulation: an in vitro study. J Physiol 376:163\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1113/jphysiol.1986.sp016147\u003c/span\u003e\u003cspan address=\"10.1113/jphysiol.1986.sp016147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLutz CS, Cornett AL (2013) Regulation of genes in the arachidonic acid metabolic pathway by RNA processing and RNA-mediated mechanisms. Wiley Interdiscip Rev RNA 4(5):593\u0026ndash;605. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/wrna.1183\u003c/span\u003e\u003cspan address=\"10.1002/wrna.1183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahley RW (2016) Central Nervous System Lipoproteins: ApoE and Regulation of Cholesterol Metabolism. Arterioscler Thromb Vasc Biol 36(7):1305\u0026ndash;1315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/ATVBAHA.116.307023\u003c/span\u003e\u003cspan address=\"10.1161/ATVBAHA.116.307023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarjenberg Z, Leng S, Tascini C et al (2023) Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis. Sci Rep 13(1):15332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-023-42321-9\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-42321-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkov PV, Ghafari M, Beer M et al (2023) The evolution of SARS-CoV-2. Nat Rev Microbiol 21(6):361\u0026ndash;379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41579-023-00878-2\u003c/span\u003e\u003cspan address=\"10.1038/s41579-023-00878-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaus M, Cuk M, Patel B et al (2017) Store-Operated Ca(2+) Entry Controls Induction of Lipolysis and the Transcriptional Reprogramming to Lipid Metabolism. Cell Metab 25(3):698\u0026ndash;712. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmet.2016.12.021\u003c/span\u003e\u003cspan address=\"10.1016/j.cmet.2016.12.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxfield FR, Wustner D (2002) Intracellular cholesterol transport. J Clin Invest 110(7):891\u0026ndash;898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI16500\u003c/span\u003e\u003cspan address=\"10.1172/JCI16500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMedicalNewsToday (2024) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medicalnewstoday.com/articles/new-definition-of-long-covid-aims-to-offer-clarity-direction\u003c/span\u003e\u003cspan address=\"https://www.medicalnewstoday.com/articles/new-definition-of-long-covid-aims-to-offer-clarity-direction\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Q, Hu X, Zhao X et al (2021) A circular network of coregulated sphingolipids dictates lung cancer growth and progression. EBioMedicine 66:103301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2021.103301\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2021.103301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoskot M, Jakobkiewicz-Banecka J, Smolinska E et al (2015) Activities of genes controlling sphingolipid metabolism in human fibroblasts treated with flavonoids. Metab Brain Dis 30(5):1257\u0026ndash;1267. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11011-015-9705-x\u003c/span\u003e\u003cspan address=\"10.1007/s11011-015-9705-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunjal NS, Sapra D, Parthasarathi KTS et al (2022) Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods. Pathogens 11(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/pathogens11020259\u003c/span\u003e\u003cspan address=\"10.3390/pathogens11020259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurillo A, Vera-Estrella R, Barkla BJ et al (2015) Identification of Host Cell Factors Associated with Astrovirus Replication in Caco-2 Cells. J Virol 89(20):10359\u0026ndash;10370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/JVI.01225-15\u003c/span\u003e\u003cspan address=\"10.1128/JVI.01225-15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavarese EP, Musci RL, Frediani L et al (2020) Ion channel inhibition against COVID-19: A novel target for clinical investigation. Cardiol J 27(4):421\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5603/CJ.a2020.0090\u003c/span\u003e\u003cspan address=\"10.5603/CJ.a2020.0090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlgun Yildizeli S, Kocakaya D, Saylan YH et al (2023) Anxiety, Depression, and Sleep Disorders After COVID-19 Infection. Cureus 15(7):e42637. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.42637\u003c/span\u003e\u003cspan address=\"10.7759/cureus.42637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOughtred R, Rust J, Chang C et al (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci 30(1):187\u0026ndash;200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/pro.3978\u003c/span\u003e\u003cspan address=\"10.1002/pro.3978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOvermyer KA, Shishkova E, Miller IJ et al (2021) Large-Scale Multi-omic Analysis of COVID-19 Severity. Cell Syst. ;12(1):23\u0026ndash;40 e27; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cels.2020.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.cels.2020.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalacios-Rapalo SN, De Jesus-Gonzalez LA, Cordero-Rivera CD et al (2021) Cholesterol-Rich Lipid Rafts as Platforms for SARS-CoV-2 Entry. Front Immunol 12:796855. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.796855\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.796855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParthasarathi KTS, Mandal S, George JP et al (2023) Aberrations in ion channels interacting with lipid metabolism and epithelial-mesenchymal transition in esophageal squamous cell carcinoma. Front Mol Biosci 10:1201459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmolb.2023.1201459\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2023.1201459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParthasarathi KTS, Munjal NS, Dey G et al (2021) A pathway map of signaling events triggered upon SARS-CoV infection. J Cell Commun Signal 15(4):595\u0026ndash;600. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12079-021-00642-2\u003c/span\u003e\u003cspan address=\"10.1007/s12079-021-00642-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerlman S, Netland J (2009) Coronaviruses post-SARS: update on replication and pathogenesis. Nat Rev Microbiol 7(6):439\u0026ndash;450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrmicro2147\u003c/span\u003e\u003cspan address=\"10.1038/nrmicro2147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePremkumar T, Sajitha Lulu S (2023) Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach. Front Med (Lausanne) 10:1151046. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2023.1151046\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2023.1151046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaveendran AV, Jayadevan R, Sashidharan S, Long COVID (2021) An overview. Diabetes Metab Syndr 15(3):869\u0026ndash;875. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dsx.2021.04.007\u003c/span\u003e\u003cspan address=\"10.1016/j.dsx.2021.04.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusso A, Biselli-Chicote PM, Kawasaki-Oyama RS et al (2018) Differential Expression of Prostaglandin I2 Synthase Associated with Arachidonic Acid Pathway in the Oral Squamous Cell Carcinoma. J Oncol 2018:6301980. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2018/6301980\u003c/span\u003e\u003cspan address=\"10.1155/2018/6301980\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u0026ndash;2504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.1239303\u003c/span\u003e\u003cspan address=\"10.1101/gr.1239303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Y, Andhey PS, Ising C et al (2021) Overexpressing low-density lipoprotein receptor reduces tau-associated neurodegeneration in relation to apoE-linked mechanisms. Neuron. ;109(15):2413\u0026ndash;2426 e2417; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuron.2021.05.034\u003c/span\u003e\u003cspan address=\"10.1016/j.neuron.2021.05.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonnweber T, Pizzini A, Nairz M et al (2018) Arachidonic Acid Metabolites in Cardiovascular and Metabolic Diseases. Int J Mol Sci 19(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms19113285\u003c/span\u003e\u003cspan address=\"10.3390/ijms19113285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk D, Gable AL, Nastou KC et al (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49(D1):D605\u0026ndash;D612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkaa1074\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaube S, Jiang M, Wobus CE (2010) Glycosphingolipids as receptors for non-enveloped viruses. Viruses 2(4):1011\u0026ndash;1049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v2041011\u003c/span\u003e\u003cspan address=\"10.3390/v2041011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrevino TN, Fogel AB, Minshall R et al (2023) Caveolin-1 mediates neuroinflammation and cognitive impairment in SARS-CoV-2 infection. bioRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2023.10.18.563024\u003c/span\u003e\u003cspan address=\"10.1101/2023.10.18.563024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrivedi UH, Cezard T, Bridgett S et al (2014) Quality control of next-generation sequencing data without a reference. Front Genet 5:111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2014.00111\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2014.00111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrushina E, Du Charme J, Parisi J et al (2006) Neurological abnormalities in caveolin-1 knock out mice. Behav Brain Res 172(1):24\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbr.2006.04.024\u003c/span\u003e\u003cspan address=\"10.1016/j.bbr.2006.04.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarughese JT, Buchanan SK, Pitt AS (2021) The Role of Voltage-Dependent Anion Channel in Mitochondrial Dysfunction and Human Disease. Cells 10(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells10071737\u003c/span\u003e\u003cspan address=\"10.3390/cells10071737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Yang P, Liu K et al (2008) SARS coronavirus entry into host cells through a novel clathrin- and caveolae-independent endocytic pathway. Cell Res 18(2):290\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/cr.2008.15\u003c/span\u003e\u003cspan address=\"10.1038/cr.2008.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong JP, Damania B (2021) SARS-CoV-2 dependence on host pathways. Science 371(6532):884\u0026ndash;885. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.abg6837\u003c/span\u003e\u003cspan address=\"10.1126/science.abg6837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu C, Liu Y, Yang Y et al (2020) Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B 10(5):766\u0026ndash;788. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apsb.2020.02.008\u003c/span\u003e\u003cspan address=\"10.1016/j.apsb.2020.02.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu P, Chen D, Ding W et al (2021) The trans-omics landscape of COVID-19. Nat Commun 12(1):4543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-021-24482-1\u003c/span\u003e\u003cspan address=\"10.1038/s41467-021-24482-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu K, Nagy PD (2015) RNA virus replication depends on enrichment of phosphatidylethanolamine at replication sites in subcellular membranes. Proc Natl Acad Sci U S A 112(14):E1782\u0026ndash;1791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1418971112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1418971112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang LG, March ZM, Stephenson RA et al (2023) Apolipoprotein E in lipid metabolism and neurodegenerative disease. Trends Endocrinol Metab 34(8):430\u0026ndash;445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tem.2023.05.002\u003c/span\u003e\u003cspan address=\"10.1016/j.tem.2023.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYong SJ (2021) Long COVID or post-COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infect Dis (Lond) 53(10):737\u0026ndash;754. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/23744235.2021.1924397\u003c/span\u003e\u003cspan address=\"10.1080/23744235.2021.1924397\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZawilska JB, Kuczynska K (2022) Psychiatric and neurological complications of long COVID. J Psychiatr Res 156:349\u0026ndash;360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpsychires.2022.10.045\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychires.2022.10.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Shao L, Lin Z et al (2022) APOE interacts with ACE2 inhibiting SARS-CoV-2 cellular entry and inflammation in COVID-19 patients. Signal Transduct Target Ther 7(1):261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41392-022-01118-4\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-01118-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong J, Sharma J, Raju R et al (2014) TSLP signaling pathway map: a platform for analysis of TSLP-mediated signaling. Database (Oxford) 2014:bau007. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/database/bau007\u003c/span\u003e\u003cspan address=\"10.1093/database/bau007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: List of hub nodes in the protein\u0026ndash;protein interaction networks\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eHub nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003eDegree centrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003eNumber of interacting ion channels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003eNumber of interacting lipid metabolism- related genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePRKACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePRKACB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePRKACG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eCYP2E1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eLPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePPARG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eCACNA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eAPOE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eCYP1A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eCYP7A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eFASN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePLPP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePLPP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePTGS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eMED1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eAPOB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003eDGAT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.4375%;\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.4062%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.7812%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34.375%;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute of Bioinformatics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Infectious disease, Pathogen, Transmembrane proteins, Lipid bilayer, Molecular targets","lastPublishedDoi":"10.21203/rs.3.rs-5224427/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5224427/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection continues to expand its horizon through the development of diverse symptoms, particularly concerning long COVID. The patients infected with the SARS-CoV-2 are being reported to develop new symptoms such as brain fog, fatigue, and other symptoms that are not limited to the respiratory system. The SARS-CoV-2 utilizes the human ion channels (HICs) and molecules involved in lipid metabolism from their entry to their egress. Here, to identify molecular alterations in HICs and lipid metabolism-related genes, transcriptomic data of 277 SARS-CoV-2 infected patients were analyzed. 287 HICs and 754 lipid metabolism-related genes were found to be differentially expressed in SARS-CoV-2 infected patients. Further, an interactome of altered HICs and lipid metabolism-related proteins with SARS-CoV-2 proteins was generated. Extensive data mining approach was employed to generate a pathway map highlighting alteration in several pathways including calcium signaling, long-term depression, and cholesterol metabolism in SARS-CoV-2 infected patients. Moreover, 17 potential drugs with known modes of action that interact with 4 altered HICs including inositol 1,4,5-triphosphate (InsP3) receptors and gap junction protein alpha 1 were identified. Most likely, these HICs are potential candidates for drug repurposing in patients infected with SARS-CoV-2 and require further experimental validation.\u003c/p\u003e","manuscriptTitle":"Identifying the interactome of altered ion channels with lipid metabolism in SARS-CoV-2 infected patients in post-COVID-19 era","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 12:56:08","doi":"10.21203/rs.3.rs-5224427/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":"aecd792f-1037-41e4-b3d4-bf5985aa0177","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38678717,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-10-10T12:56:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-10 12:56:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5224427","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5224427","identity":"rs-5224427","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}