Aims The molecular pathogenesis of COVID-19 is similar to other coronavirus (CoV) infections viz. severe acute respiratory syndrome (SARS) in human. Due to scarcity of the suitable treatment strategy, the present study was undertaken to explore host protein(s) targeted by potent repurposed drug(s) in COVID-19. Materials and methods The differentially expressed genes (DEGs) were identified from microarray data repository of SARS-CoV patient blood. The repurposed drugs for COVID-19 were selected from available literature. Using DEGs and drugs, the protein-protein interaction (PPI) and chemo-protein interaction (CPI) networks were constructed and combined to develop an interactome model of PPI-CPI network. The top-ranked sub-network with its hub-bottleneck nodes were evaluated with their functional annotations. Key findings A total of 120 DEGs and 65 drugs were identified. The PPI-CPI network (118 nodes and 293 edges) exhibited a top-ranked sub-network (35 nodes and 174 connectivities) with 12 hub-bottleneck nodes having two drugs chloroquine and melatonin in association with 10 proteins corresponding to six upregulated and four downregulated genes. Two drugs interacted directly with the hub-bottleneck node i.e. matrix metallopeptidase 9 (MMP9), a host protein corresponding to its upregulated gene. MMP9 showed functional annotations associated with neutrophil mediated immunoinflammation. Moreover, literature survey revealed that angiotensin converting enzyme 2, a membrane receptor of SARS-CoV-2 virus, might have functional cooperativity with MMP9 and a possible interaction with both drugs. Significance The present study reveals that between chloroquine and melatonin, melatonin appears to be more promising repurposed drug against MMP9 for better immunocompromisation in COVID-19.

COVID-19 develops certain neurological symptoms, the molecular pathophysiology of which is obscure. In the present study, two networks were constructed and their hub-bottleneck and driver nodes were evaluated to consider them as 'target genes' followed by identifying 'candidate genes' and their associations with neurological phenotypes of COVID-19. A tripartite network was first constructed using literature-based neurological symptoms of COVID-19 as input. The target genes evaluated therefrom were then used as query genes to identify the co-expressed genes from the RNA-sequence data of the frontal cortex of COVID-19 patients using pair-wise mutual information to genes. A 'combined gene network' (CGN) was constructed with 189 genes selected from TN and 225 genes co-expressed in COVID-19. Total 44 'target genes' evaluated from both networks and their connecting genes in respective networks were analyzed functionally by measuring pair-wise 'semantic similarity scores' (SSS) and finding Enrichr annotation terms against a set of genes. A new integrated 'weighted harmonic mean score' was formulated using SSS and STRING-based 'combined score' to select 21 gene-pairs among 'target genes' that provided 21 'candidate genes' with their properties as 'indispensable driver nodes' of CGN. Finally, six pairs providing seven prevalent candidate genes (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) exhibited direct linkage with the neurological phenotypes under tumour/cancer, cellular signalling, neurodegeneration and neurodevelopmental diseases. The other phenotypes under behaviour/cognitive and motor dysfunctions showed indirect associations with the former genes through other candidate genes. The pathophysiology of 'prevalent candidate genes' has been discussed for better interpretation of neurological manifestation in COVID-19.

Abstract‘Tripartite network’ (TN) and ‘combined gene network’ (CGN) were constructed and their hub-bottleneck and driver nodes (44 genes) were evaluated as ‘target genes’ (TG) to identify 21 ‘candidate genes’ (CG) and their relationship with neurological manifestations of COVID-19. TN was developed using neurological symptoms of COVID-19 found in literature. Under query genes (TG of TN), co-expressed genes were identified using pair-wise mutual information to genes available in RNA-Seq autopsy data of frontal cortex of COVID-19 victims. CGN was constructed with genes selected from TN and co-expressed in COVID-19. TG and their connecting genes of respective networks underwent functional analyses through findings of their enrichment terms and pair-wise ‘semantic similarity scores’ (SSS). A new integrated ‘weighted harmonic mean score’ was formulated assimilating values of SSS and STRING-based ‘combined score’ of the selected TG-pairs, which provided CG-pairs with properties of CGs as co-expressed and ‘indispensable nodes’ in CGN. Finally, six pairs sharing seven ‘prevalent CGs’ (ADAM10, ADAM17, AKT1, CTNNB1, ESR1, PIK3CA, FGFR1) showed linkages with the phenotypes (a) directly under neurodegeneration, neurodevelopmental diseases, tumour/cancer and cellular signalling, and (b) indirectly through other CGs under behavioural/cognitive and motor dysfunctions. The pathophysiology of ‘prevalent CGs’ has been discussed to interpret neurological phenotypes of COVID-19.