Exploring SARS-CoV2 host-pathogen interactions and associated fungal infections cross-talk: Screening of targets and understanding pathogenesis.

The COVID-19 associated opportunistic fungal infections have posed major challenges in recent times. Global scientific efforts have identified several SARS-CoV2 host-pathogen interactions in a very short time span. However, information about the molecular basis of COVID-19 associated opportunistic fungal infections is not readily available. Previous studies have identified a number of host targets involved in these opportunistic fungal infections showing association with COVID-19 patients. We screened host targets involved in COVID-19-associated opportunistic fungal infections, in addition to host-pathogen interaction data of SARS-CoV2 from well-known and widely used biological databases. Venn diagram was prepared to screen common host targets involved in studied COVID-19-associated fungal infections. Moreover, an interaction network of studied disease targets was prepared with STRING to identify important targets on the basis of network biological parameters. The host-pathogen interaction (HPI) map of SARS-CoV2 was also prepared and screened to identify interactions of the virus with targets involved in studied fungal infections. Pathway enrichment analysis of host targets involved in studied opportunistic fungal infections and the subset of those involved in SARS-CoV2 HPI were performed separately. This data-based analysis screened six common targets involved in all studied fungal infections, among which CARD9 and CYP51A1 were involved in host-pathogen interactions with SARS-CoV2. Moreover, several signaling pathways such as integrin signaling were screened, which were associated with disease targets involved in SARS-CoV2 HPI. The results of this study indicate several host targets deserving detailed investigation to develop strategies for the management of SARS-CoV2-associated fungal infections.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

Comparative Host-Pathogen Interaction Analyses of SARS-CoV2 and Aspergillus fumigatus, and Pathogenesis of COVID-19-Associated Aspergillosis.

COVID-19 caused a global catastrophe with a large number of cases making it one of the major pandemics of the human history. The clinical presentations of the disease are continuously challenging healthcare workers with the variation of pandemic waves and viral variants. Recently, SARS-CoV2 patients have shown increased occurrence of invasive pulmonary aspergillosis infection even in the absence of traditional risk factors. The mechanism of COVID-19-associated aspergillosis is not completely understood and therefore, we performed this system biological study in order to identify mechanistic implications of aspergillosis susceptibility in COVID-19 patients and the important targets associated with this disease. We performed host-pathogen interaction (HPI) analysis of SARS-CoV2, and most common COVID-19-associated aspergillosis pathogen, Aspergillus fumigatus, using in silico approaches. The known host-pathogen interactions data of SARS-CoV2 was obtained from BIOGRID database. In addition, A. fumigatus host-pathogen interactions were predicted through homology modeling. The human targets interacting with both pathogens were separately analyzed for their involvement in aspergillosis. The aspergillosis human targets were screened from DisGeNet and GeneCards. The aspergillosis targets involved in both HPI were further analyzed for functional overrepresentation analysis using PANTHER. The results indicate that both pathogens interact with a number of aspergillosis targets and altogether they recruit more aspergillosis targets in host-pathogen interaction than alone. Common aspergillosis targets involved in HPI with both SARS-CoV2 and A. fumigatus can indicate strategies for the management of both conditions by modulating these common disease targets.

Systematic Network and Meta-analysis on the Antiviral Mechanisms of Probiotics: A Preventive and Treatment Strategy to Mitigate SARS-CoV-2 Infection.

With the alarming rise of infected cases and deaths, COVID-19 is a pandemic, affecting 220 countries worldwide. Until now, no specific treatment is available against SARS-CoV-2. The causal virus SARS-CoV-2 primarily infects lung cells, leading to respiratory illness ranging in severity from the common cold to deadly pneumonia. This, with comorbidities, worsens the clinical outcome, particularly for immunosuppressed individuals with COVID-19. Interestingly, the commensal gut microbiota has been shown to improve lung infections by modulating the immune system. Therefore, fine-tuning of the gut microbiome with probiotics could be an alternative strategy for boosting immunity and treating COVID-19. Here, we present a systematic biological network and meta-analysis to provide a rationale for the implementation of probiotics in preventing and/or treating COVID-19. We have identified 90 training genes from the literature analysis (according to PRISMA guidelines) and generated an association network concerning the candidate genes linked with COVID-19 and probiotic treatment. The functional modules and pathway enrichment analysis of the association network clearly show that the application of probiotics could have therapeutic effects on ACE2-mediated virus entry, activation of the systemic immune response, nlrp3-mediated immunomodulatory pathways, immune cell migration resulting in lung tissue damage and cardiovascular difficulties, and altered glucose/lipid metabolic pathways in the disease prognosis. We also demonstrate the potential mechanistic domains as molecular targets for probiotic applications to combat the viral infection. Our study, therefore, offers probiotics-mediated novel preventive and therapeutic strategies for COVID-19 warfare.

The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions.

The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open-access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low-throughput studies and large high-throughput datasets. BioGRID also captures protein post-translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built-in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin-proteasome system, as well as specific disease areas, such as for the SARS-CoV-2 virus that causes COVID-19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome-wide CRISPR/Cas9-based genetic screens. BioGRID-ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta-databases.