Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19.

'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.

Matrix metallopeptidase 9 as a host protein target of chloroquine and melatonin for immunoregulation in COVID-19: A network-based meta-analysis.

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.