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

doi:10.21203/rs.3.rs-5224427/v1

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

https://doi.org/10.21203/rs.3.rs-5224427/v1

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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

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 Ca²⁺ 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.

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.

3.1 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; Ca²⁺, 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).

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.

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.

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.

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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

The authors declare no competing interests.

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

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Data collection

2.2 Data processing

2.3 Identification of differentially expressed genes

2.4 Enrichment Analysis for Functional Clustering of DEGs

2.5 Protein-protein interaction networks

2.5.1 Altered HICs with lipid metabolism-related proteins

2.5.2 Critical subnetworks in PPINs using the hub nodes

2.5.3 SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins

2.6 Generation of pathway map of HICs and lipid metabolism-related proteins

2.7 Identification of drugs interacting with HICs

3 Results

3.1 Differentially expressed HICs and Lipid metabolism-related genes in SARS-CoV-2-infected patients

3.2 Functional clustering of DEGs

3.3 Densely interconnected subnetworks through PPINs

3.4 PPINs of SARS-CoV-2 proteins with HICs and lipid metabolism-related proteins

3.5 Pathway map of altered HICs and their interactions with lipid metabolism-related proteins

3.6 Drugs interacting with HICs:

4 Discussion

5 Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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