Parthasarathi, Kaushik

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.

Gastric cancer (GC) remains a leading cause of cancer-related mortality globally. This is due to the fact that majority of the cases of GC are diagnosed at an advanced stage when the treatment options are limited and prognosis is poor. The diffuse subtype of gastric cancer (DGC) under Lauren's classification is more aggressive and usually occurs in younger patients than the intestinal subtype. The concept of personalized medicine is leading to the identification of multiple biomarkers in a large variety of cancers using different combinations of omics technologies. Proteomic changes including post-translational modifications are crucial in oncogenesis. We analyzed the phosphoproteome of DGC by using paired fresh frozen tumor and adjacent normal tissue from five patients diagnosed with DGC. We found proteins involved in the epithelial-to-mesenchymal transition (EMT), c-MYC pathway, and semaphorin pathways to be differentially phosphorylated in DGC tissues. We identified three kinases, namely, bromodomain adjacent to the zinc finger domain 1B ( BAZ1B ), WNK lysine-deficient protein kinase 1 ( WNK1 ), and myosin light-chain kinase ( MLCK ) to be hyperphosphorylated, and one kinase, AP2-associated protein kinase 1 ( AAK1 ), to be hypophosphorylated. LMNA hyperphosphorylation at serine 392 (S392) was demonstrated in DGC using immunohistochemistry. Importantly, we have detected heparin-binding growth factor ( HDGF ), heat shock protein 90 ( HSP90 ), and FTH1 as potential therapeutic targets in DGC, as drugs targeting these proteins are currently under investigation in clinical trials. Although these new findings need to be replicated in larger study samples, they advance our understanding of signaling alterations in DGC, which could lead to potentially novel actionable targets in GC.

Uncontrolled growth of breast cells due to altered gene expression is a key feature of breast cancer. Alterations in the expression of ion channels lead to variations in cellular activities, thus contributing to attributes of cancer hallmarks. Changes in the expression levels of ion channels were observed as a consequence of EMT. Additionally, ion channels were reported in the activation of EMT and maintenance of a mesenchymal phenotype. Here, to identify altered ion channels in breast cancer patients, differential gene expression and weighted gene co-expression network analyses were performed using transcriptomic data. Protein–protein interactions network analysis was carried out to determine the ion channels interacting with hub EMT-related genes in breast cancer. Thirty-two ion channels were found interacting with twenty-six hub EMT-related genes. The identified ion channels were further correlated with EMT scores, indicating mesenchymal phenotype. Further, the pathway map was generated to represent a snapshot of deregulated cellular processes by altered ion channels and EMT-related genes. Kaplan–Meier five-year survival analysis and Cox regressions indicated the expression of CACNA1B, ANO6, TRPV3, VDAC1 and VDAC2 to be potentially associated with poor survival. Deregulated ion channels correlate with EMT-related genes and have a crucial role in breast cancer-associated tumorigenesis. Most likely, they are potential candidates for the determination of prognosis in patients with breast cancer.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the protracted COVID-19 pandemic. Its high transmission rate and pathogenicity led to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing a higher accuracy for host-SARS-CoV-2 protein–protein interactions (PPIs). In this study, PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were trained on the PPI-MetaGO algorithm. PPI networks (PPINs) and a signaling pathway map of HICs with SARS-CoV-2 proteins were generated. Additionally, various U.S. food and drug administration (FDA)-approved drugs interacting with the potential HICs were identified. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient score (MCC) and 84.09% F1 score. Several host pathways were found to be altered, including calcium signaling and taste transduction pathway. Potential HICs could serve as an initial set to the experimentalists for further validation. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs that may provide a better therapeutic management strategy for infection caused by SARS-CoV-2.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the worldwide COVID-19 pandemic which began in 2019. It has a high transmission rate and pathogenicity leading to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning (ML)-based algorithms are providing higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, predictions of PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were performed using PPI-MetaGO algorithm. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% Matthews correlation coefficient (MCC) score and 84.09% F1 score. Thereafter, PPI networks of SARSCoV-2 proteins with HICs were generated. Furthermore, biological pathway analysis of HICs interacting with SARS-CoV-2 proteins showed the involvement of six pathways, namely inflammatory mediator regulation of transient receptor potential (TRP) channels, insulin secretion, renin secretion, gap junction, taste transduction and apelin signaling pathway. Our analysis suggests that transient receptor potential cation channel subfamily M member 4 (TRPM4), transient receptor potential cation channel subfamily A member 1 (TRPA1), gap junction protein alpha 1 (GJA1), potassium calcium-activated channel subfamily N member 4 (KCNN4), acid sensing ion channel subunit 1 (ASIC1) and inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) could serve as an initial set to the experimentalists for further validation. Additionally, various US food and drug administration (FDA) approved drugs interacting with the potential HICs were also identified. The study also reinforcesthe drug repurposing approach for the development of host directed antiviral drugs.

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the worldwide COVID-19 pandemic which began in 2019. It has a high transmission rate and pathogenicity leading to health emergencies and economic crisis. Recent studies pertaining to the understanding of the molecular pathogenesis of SARS-CoV-2 infection exhibited the indispensable role of ion channels in viral infection inside the host. Moreover, machine learning-based algorithms are providing higher accuracy for host-SARS-CoV-2 protein-protein interactions (PPIs). In this study, predictions of PPIs of SARS-CoV-2 proteins with human ion channels (HICs) were performed using PPI-MetaGO algorithm. The PPIs were predicted with 82.71% accuracy, 84.09% precision, 84.09% sensitivity, 0.89 AUC-ROC, 65.17% MCC score and 84.09% F1 score. Thereafter, PPI networks of SARS-CoV-2 proteins with HICs were generated. Furthermore, biological pathway analysis of HICs interacting with SARS-CoV-2 proteins showed the involvement of six pathways, namely inflammatory mediator regulation of TRP channels, insulin secretion, renin secretion, gap junction, taste transduction and apelin signaling pathway. The inositol 1,4,5-trisphosphate receptor 1 (ITPR1) and transient receptor potential cation channel subfamily A member 1 (TRPA1) were identified as potential target proteins. Various FDA approved drugs interacting with ITPR1 and TRPA1 are also available. It is anticipated that targeting ITPR1 and TRPA1 may provide a better therapeutic management of infection caused by SARS-CoV-2. The study also reinforces the drug repurposing approach for the development of host directed antiviral drugs.