Pathway enrichment analysis of virus-host interactome and prioritization of novel compounds targeting the spike glycoprotein receptor binding domain-human angiotensin-converting enzyme 2 interface to combat SARS-CoV-2

Establishment of a Yeast Two-Hybrid-Based High-Throughput Screening Model for Selection of SARS-CoV-2 Spike-ACE2 Interaction Inhibitors

The recent coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has exerted considerable impact on global health. To prepare for rapidly mutating viruses and for the forthcoming pandemic, effective therapies targeting the critical stages of the viral life cycle need to be developed. Viruses are dependent on the interaction between the receptor-binding domain (RBD) of the viral Spike (S) protein (S-RBD) and the angiotensin-converting enzyme 2 (ACE2) receptor to efficiently establish infection and the following replicate. Targeting this interaction provides a promising strategy to inhibit the entry process of the virus, which in turn has both preventive and therapeutic effects. In this study, we developed a robust and straightforward assay based on the Yeast-Two Hybrid system (Y2H) for identifying inhibitors targeting the S-RBD-ACE2 interaction of SARS-CoV-2. Through high-throughput screening, two compounds were identified as potential entry inhibitors. Among them, IMB-1C was superior in terms of pseudovirus entry inhibition and toxicity. It could bind to both ACE2 and S-RBD and induce conformational change in the S-RBD+ACE2 complex. This is the first study to verify the feasibility of utilizing the Y2H system to discover potent SARS-CoV-2 inhibitors targeting the receptor recognition stage. This approach may also be applied in the discovery of other virus receptor recognition inhibitors.

The effect of quercetin supplementation on clinical outcomes in COVID-19 patients: A systematic review and meta-analysis

Coronavirus disease (COVID-19) affects both the respiratory system and the body as a whole. Natural molecules, such as flavonoid quercetin, as potential treatment methods to help patients combat COVID-19. The aim of this systematic review and meta-analysis is to give a comprehensive overview of the impact of quercetin supplementation on inflammatory factors, hospital admission, and mortality of patients with COVID-19. The search has been conducted on PubMed, Scopus, Web of Science, EMBASE, and the Cochrane Library using relevant keywords until August 25, 2023. We included randomized controlled trials (RCTs) comparing COVID-19 patients who received quercetin supplementation versus controls. We included five studies summarizing the evidence in 544 patients. Meta-analysis showed that quercetin administration significantly reduced LDH activity (standard mean difference (SMD): -0.42, 95% CI: -0.82, -0.02, I 2 = 48.86%), decreased the risk of hospital admission by 70% (RR: 0.30, 95% CI: 0.14, 0.62, I 2 = 00.00%), ICU admission by 73% (RR: 0.27, 95% CI: 0.09, 0.78, I 2 = 20.66%), and mortality by 82% (RR: 0.18, 95% CI: 0.03, 0.98, I 2 = 00.00%). No significant changes in CRP, D-dimmer, and ferritin were found between groups. Quercetin was found to significantly reduce LDH levels and decrease the risk of hospital and ICU admission and mortality in patients with COVID-19 infection.

Klarigi: Characteristic explanations for semantic biomedical data

Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such as biosample gene expression profiles or patient phenotypes, and is useful for a range of tasks including differential diagnosis and causative variant prioritisation. These approaches, however, usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they are not designed to solve the problem of deriving cohesive, characteristic, and discriminatory sets of classes for entity groups. We have developed a new tool, called Klarigi, which introduces multiple scoring heuristics for identification of classes that are both compositional and discriminatory for groups of entities annotated with ontology classes. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets, in addition to significance testing apposite to traditional enrichment approaches. We describe the design and implementation of Klarigi, including its scoring and explanation determination methods, and evaluate its use in application to two test cases with clinical significance, comparing and contrasting methods and results with literature-based and enrichment analysis methods. We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. We conclude that Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data.

Cordycepin: a bioactive metabolite of Cordyceps militaris and polyadenylation inhibitor with therapeutic potential against COVID-19

Spike protein and main proteases of SARS-CoV-2 have been identified as potential therapeutic targets and their inhibition may lead to the reticence of viral entry and replication in the host body. Despite several efforts; till now no specific drugs are available to treat SARS-CoV-2. Considering all these challenges, the main objective of the present study was to establish therapeutic potential of cordycepin against COVID-19 as a conventional therapeutic strategy. In the present study; molecular interaction study was performed to assess potential binding affinity of cordycepin with SARS-CoV-2 target proteins using computational approach. Additionally, network pharmacology was used to understand cordycepin-protein interactions and their associated pathways in human body. Cordycepin is under clinical trial (NCT00709215) and possesses structural similarity with adenosine except that, it lacks a 3' hydroxyl group in its ribose moiety and hence it served as a poly(A) polymerase inhibitor and terminate premature protein synthesis. Additionally, it is known that functional RNAs of SARS-CoV-2 genome are highly 3'-plyadenylated and leading to synthesis of all viral proteins and if cordycepin can destabilize SARS-CoV-2 RNAs by inhibiting polyadenylation process then it may step forward in terms of inhibition of viral replication and multiplication in the host. Moreover, cordycepin showed strong binding affinity with SARS-CoV-2 spike protein (-145.3) and main proteases (-180.5) that further corroborate therapeutic potential against COVID-19. Since cordycepin has both pre-clinical and clinical information about antiviral activities, therefore; it is suggested to the world community to undertake repurposing cordycepin to test efficacy and safety for the treatment of COVID-19.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.

A computational analysis of molecular evolution for virulence genes of zoonotic novel coronavirus (COVID-19)

Zoonotic Novel coronavirus disease 2019 (COVID-19) is highly pathogenic and transmissible considered as emerging pandemic disease. The virus belongs from a large virus Coronaviridae family affect respiratory tract of animal and human likely originated from bat and homology to SARA-CoV and MERS-CoV. The virus consists of single-stranded positive genomic RNA coated by nucleocapsid protein. The rate of mutation in any virulence gene may influence the phenomenon of host radiation. We have studied the molecular evolution of selected virulence genes (HA, N, RdRP and S) of novel COVID-19. We used a site-specific comparison of synonymous (silent) and non-synonymous (amino acid altering) nucleotide substitutions. Maximum Likelihood genealogies based on differential gamma distribution rates were used for the analysis of null and alternate hypothesis. The null hypothesis was found more suitable for the analysis using Likelihood Ratio Test (LRT) method, confirming higher rate of substitution. The analysis revealed that RdRP gene had the fastest rate evolution followed by HA gene. We have also reported the new motifs for different virulence genes, which are further useful to design new detection and diagnosis kit for COVID -19.

Proposition of a new allosteric binding site for potential SARS-CoV-2 3CL protease inhibitors by utilizing molecular dynamics simulations and ensemble docking

The SARS-CoV-2 3CL protease (3CLpro) shows a high similarity with 3CL proteases of other beta-coronaviruses, such as SARS and MERS. It is the main enzyme involved in generating various non-structural proteins that are important for viral replication and is one of the most important proteins responsible for SARS-CoV-2 virulence. In this study, we have conducted an ensemble docking of molecules from the DrugBank database using both the crystallographic structure of the SARS-CoV-2 3CLpro, as well as five conformations obtained after performing a cluster analysis of a 300 ns molecular dynamics (MD) simulation. This procedure elucidated the inappropriateness of the active site for non-covalent inhibitors, but it has also shown that there exists an additional, more favorable, allosteric binding site, which could be a better target for non-covalent inhibitors, as it could prevent dimerization and activation of SARS-CoV-2 3CLpro. Two such examples are radotinib and nilotinib, tyrosine kinase inhibitors already in use for treatment of leukemia and which binding to the newly found allosteric binding site was also confirmed using MD simulations. Communicated by Ramaswamy H. Sarma.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.

Nucleocapsid and Spike Proteins of SARS-CoV-2 Drive Neutrophil Extracellular Trap Formation

Patients with severe coronavirus disease 2019 (COVID-19) demonstrate dysregulated immune responses including exacerbated neutrophil functions. Massive neutrophil infiltrations accompanying neutrophil extracellular trap (NET) formations are also observed in patients with severe COVID-19. However, the mechanism underlying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced NET formation has not yet been elucidated. Here we show that 2 viral proteins encoded by SARS-CoV-2, the nucleocapsid protein and the whole spike protein, induce NET formation from neutrophils. NET formation was ROS-independent and was completely inhibited by the spleen tyrosine kinase inhibition. The inhibition of p38 MAPK, protein kinase C, and JNK signaling pathways also inhibited viral protein-induced NET formation. Our findings demonstrate one method by which SARS-CoV-2 evades innate immunity and provide a potential target for therapeutics to treat patients with severe COVID-19.

A review of monoclonal antibodies in COVID-19: Role in immunotherapy, vaccine development and viral detection

The harmful COVID-19 pandemic caused by the SARS-CoV-2 coronavirus imposes the scientific community to develop or find conventional curative drugs, protective vaccines, or passive immune strategies rapidly and efficiently. Passive immunity is based on recovering hyper-immune plasma from convalescent patients, or monoclonal antibodies with elevated titer of neutralizing antibodies with high antiviral activity, that have potential for both treatment and prevention. In this review, we focused on researching the potentiality of monoclonal antibodies for the prevention and treatment of COVID-19 infection. Our research review includes antibody-based immunotherapy, using human monoclonal antibodies targeting SARS-CoV-2 viral protein regions, specifically the spike protein regions, and using hyper-immune plasma from convalescent COVID-19 patients, in which monoclonal antibodies act as immunotherapy for the cytokine storm syndrome associated with the COVID-19 infection. In addition, we will demonstrate the role of the monoclonal antibodies in the development of candidate vaccines for SARS-CoV-2. Moreover, the recent progress of the diagnostic mouse monoclonal antibodies' role will be highlighted, as an accurate and rapid diagnostic assay, in the antigen detection of SARS-CoV-2. In brief, the monoclonal antibodies are the potential counter measures that may control SARS-CoV-2, which causes COVID-19 disease, through immunotherapy and vaccine development, as well as viral detection.