Study on potential common action and mechanism of Reduning Injection against SARS, MERS and COVID-19 based on network pharmacology and molecular docking technology.

Objective To explore the potential common mechanism and active ingredients of Reduning Injection against SARS, MERS and COVID-19 through network pharmacology and molecular docking technology. Methods The TCMSP database was used to retrieve the chemical components and targets of Artemisiae Annuae Herba, Lonicerae Japonicae Flos and Gardeniae Fructus in Reduning Injection. The gene corresponding to the target was searched by UniProt database, and Cytoscape 3.8.2 was used to build a medicinal material-compound-target (gene) network. Three coronavirus-related targets were collected in the Gene Cards database with the key words of "SARS""MERS" and "COVID-19", and common target of three coronavirus infection diseases were screened out through Venny 2.1.0 database. The common targets of SARS, MERS and COVID-19 were intersected with the targets of Reduning Injection, and the common targets were selected as research targets. Protein-protein interaction (PPI) network map were constructed by Cytoscape3.8.2 software after importing the common targets into the STRING database to obtain data. R language was used to carry out GO biological function enrichment analysis and KEGG signaling pathway enrichment analysis, histograms and bubble charts were drew, and component-target-pathway network diagrams was constructed. The key compounds in the component-target-pathway network were selected for molecular docking with important target proteins, novel coronavirus (SARS-CoV-2) 3CL hydrolase, and angiotensin-converting enzyme II (ACE2). Results 31 active compounds and 207 corresponding targets were obtained from Reduning Injection. 2 453 SARS-related targets, 805 MERS-related targets, 2 571 COVID-19-related targets, and 786 targets for the three diseases. 11 common targets with Reduning Injection: HSPA5, CRP, MAPK1, HMOX1, TGFB1, HSP90AA1, TP53, DPP4, CXCL10, PLAT, PRKACA. GO function enrichment analysis revealed 995 biological processes (BP), 71 molecular functions (MF), and 31 cellular components (CC). KEGG pathway enrichment analysis screened 99 signal pathways (P < 0.05), mainly related to prostate cancer, fluid shear stress and atherosclerosis, hepatocellular carcinoma, proteoglycans in cancer, lipid and atherosclerosis, human T-cell leukemia virus 1 infection, MAPK signaling pathway, etc. The molecular docking results showed that the three core active flavonoids of quercetin, luteolin, and kaempferol in Reduning Injection had good affinity with key targets MAPK1, PRKACA, and HSP90AA1, and the combination of the three active compounds with SARS-CoV-2 3CL hydrolase and ACE2 was less than the recommended chemical drugs. Conclusion Reduning Injection has potential common effects on the three diseases of SARS, MERS and COVID-19. This effect may be related to those active compounds such as quercetin, luteolin, and kaempferol acting on targets such as MAPK1, PRKACA, HSP90AA1 to regulate multiple signal pathways and exert anti-virus, suppression of inflammatory storm, and regulation of immune function.Copyright © 2022 Drug Evaluation Research. All rights reserved.

Exploration of potential mechanism of SARS-CoV-2 induced immune inflammatory response leading to progression of atherosclerosis and prediction of traditional Chinese medicine for preventing and treating based on bioinformatics.

Objective To explore the core targets and important pathways of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) induced atherosclerosis (AS) progression from the perspective of immune inflammation, so as to predict the potential prevention and treatment of traditional Chinese medicine (TCM). Methods Microarray data were obtained from the Gene Expression Omnibus (GEO) database for coronavirus disease 2019 (COVID-19) patients and AS patients, and the "limmar" and "Venn" packages were used to screen out the common differentially expressed genes (DEGs) genes in both diseases. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed on the common DEGs to annotate their functions and important pathways. The two gene sets were scored for immune cells and immune function to assess the level of immune cell infiltration. The protein-protein interaction (PPI) network was constructed by STRING database, and the CytoHubba plug-in of Cytoscape was used to identify the hub genes. Two external validation datasets were introduced to validate the hub genes and obtain the core genes. Immuno-infiltration analysis and gene set enrichment analysis (GSEA) were performed on the core genes respectively. Finally the potential TCM regulating the core genes were predicted by Coremine Medical database. Results A total of 7898 genes related to COVID-19, 471 genes related to AS progression;And 51 common DEGs, including 32 highly expressed genes and 19 low expressed genes were obtained. GO and KEGG analysis showed that common DEGs, which were mainly localized in cypermethrin-encapsulated vesicles, platelet alpha particles, phagocytic vesicle membranes and vesicles, were involved in many biological processes such as myeloid differentiation factor 88 (MyD88)-dependent Toll-like receptor signaling pathway transduction, interleukin-8 (IL-8) production and positive regulation, IL-6 production and positive regulation to play a role in regulating nicotinamide adenine dinucleotide phosphate oxidase activity, Toll-like receptor binding and lipopeptide and glycosaminoglycan binding through many biological pathways, including Toll-like receptor signaling pathways, neutrophil extracellular trap formation, complement and coagulation cascade reactions. The results of immune infiltration analysis demonstrated the state of immune microenvironment of COVID-19 and AS. A total of 5 hub genes were obtained after screening, among which Toll-like receptor 2 (TLR2), cluster of differentiation 163 (CD163) and complement C1q subcomponent subunit B (C1QB) genes passed external validation as core genes. The core genes showed strong correlation with immune process and inflammatory response in both immune infiltration analysis and GSEA enrichment analysis. A total of 35 TCMs, including Chuanxiong (Chuanxiong Rhizoma), Taoren (Persicae Semen), Danggui (Angelicae Sinensis Radix), Huangqin (Scutellariae Radix), Pugongying (Taraxaci Herba), Taizishen (Pseudostellariae Radix), Huangjing (Polygonati Rhizoma), could be used as potential therapeutic agents. Conclusion TLR2, CD163 and C1QB were the core molecules of SARS-CoV-2-mediated immune inflammatory response promoting AS progression, and targeting predicted herbs were potential drugs to slow down AS progression in COVID-19 patients.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis.

Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.

THE SPIKE OF SARS-CoV-2 VARIANTS ALLOWS FOR MORE EFFICIENT COUNTERACTION OF BST2

Background: BST2/Tetherin is an interferon-stimulated gene with antiviral activity against enveloped viruses. Particularly, BST2 tethers virions at their site of assembly, preventing their release and spread. In addition to this primary role, BST2 is as an important bridge between the innate and adaptive immune system, since (i) BST2 routes tethered particles to lysosomes, which generates viral breakdown products that engage pattern recognition receptors;and (ii) trapped virions facilitate antibody-dependent cell-mediated cytotoxicity (ADCC). In turn, viruses have evolved mechanisms to bypass BST2, either by targeting BST2 for proteasomal/lysosomal degradation or by removing BST2 from sites of virion assembly. However, the role of BST2 in SARS-CoV-2 replication, spread, evolution, and pathogenesis remains largely unknown. Method(s): The antiviral potential of BST2 against SARS-CoV-2 was investigated by infecting different SARS-CoV-2 isolates (Hong Kong, Alpha, Beta, Delta, and Omicron) in BST2+ and BST2- cells. Culture supernatants were collected to assess virion production by ELISA and infectivity by TCID50. Infected cells were analyzed by western blot and flow cytometry to examine viral and cellular protein levels, including BST2. Transfection of individual SARS-CoV-2 ORFs and mutagenesis studies allowed us to identify the genes that the virus uses to downregulate BST2. Immunoprecipitation assays revealed protein-protein interactions and changes in ubiquitination patterns. Experiments with proteasomal and lysosomal inhibitors furthered our mechanistic understanding of how SARS-CoV-2 counteracts BST2. Finally, fluorescence microscopy studies uncovered changes in the subcellular distribution of BST2 in SARS-CoV-2 infected cells. Result(s): While BST2 reduces virion release, SARS-CoV-2 has evolved to counteract this effect. Specifically, SARS-CoV-2 uses the Spike to interact with BST2, sequester the protein at perinuclear locations, and ultimately route it for lysosomal degradation. By surveying different SARS-CoV-2 variants of concern (Alpha-Omicron), we found that each variant is more efficient than the previously circulating strain at downregulating BST2 and facilitating virion production, and that mutations in the Spike account for their enhanced BST2 antagonism. Conclusion(s): As part of its adaptation to humans, SARS-CoV-2 is improving its capacity to counteract BST2, highlighting that BST2 antagonism is important for SARS-CoV-2 infectivity and transmission.

Isolation and Characterization of Protein Complexes of Overexpressed Human Serum Amyloid A from Cytokines-Induced Hepg2 Cells

Serum Amyloid A (SAA) is an apolipoprotein found in the serum of many vertebrate species and is associated with the acute-phase reaction in the body with expression levels reaching up to a 1000-fold increase. The loss of its alpha-helix conformation during its expression peak is directly linked to secondary amyloidosis. Recent studies have been suggested to play a role in cholesterol and HDL metabolism, retinol transport and tumor pathogenesis. Moreover, high SAA concentration in blood have been correlated with severe symptoms or death in patients with COVID-19. However, how this protein is involved in so many diseases is uncertain or not completely understood. Therefore, the purpose of this research is to determine which protein-protein interactions with SAA occur in human cells, and to predict its biochemical role based on new discovered complexes. Two major isoforms overexpressed during an acutephase reaction, human SAA1 and SAA2, are the focus of this study. Both are primarily produced in hepatocytes. HepG2 cells were cultured and induced with interleukin-1b, interleukin-6, LPS and retinol. Protein complexes associated with SAA will be isolated through a co-Immunoprecipitation technique, resolved by SDS-PAGE, and characterized by mass spectrometry. Our hypothesis focus on those protein complexes with SAA to explain how this protein lead other undiscovered metabolic pathways involved in both cellular and survival regulation. Special thanks to The Science and Technology Competency & Education Core (Stce) for Undergraduate and Graduate Junior Research Associates Working Program from the Puerto Rico IDeA Network Biomedical Research Excellence for funding part of this research.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.

SARS-CoV-2 USES ENDOSOMAL MEMBRANES TO BUILD REPLICATION ORGANELLES

Background: The health emergency caused by the COVID-19 pandemic has evidenced that the frequency of spillover episodes of viruses infecting bats to other species, including humans, has significantly increased compared to previous decades. Besides SARS-CoV-2, six other human coronaviruses (NL63, 229E, OC43, HKU1, SARS-CoV and MERS-CoV) emerged in the 20th and 21st century, most likely because of cross-species transmission events from bats. While many of these coronaviruses cause mild respiratory infections, MERS-CoV, SARS-CoV and SARS-CoV-2 can cause severe respiratory distress, particularly in immunocompromised individuals. However, unlike SARS-CoV and MERS-CoV, SARS-CoV-2 is highly contagious, very stable, with many person-to-person transmissions, which can occur even before individuals exhibit any symptoms. While vaccines are readily available, the emergence of new SARS-CoV-2 variants along with the increasing incidence of individuals developing long COVID urge to develop antivirals specific to treat COVID-19. To reach this goal, we need to have a working knowledge of the host-SARS-CoV-2 interactions to identify targets for therapeutic intervention. Method(s): Following that rationale, we focused on understanding how SARSCoV- 2 generates replication organelles (ROs). All coronaviruses need to remodel cellular membranes to create these structures to allow the active replication and transcription of their genome. Due to their relevance for virus replication, disabling RO formation represents a promising strategy to fight SARS-CoV-2. However, the biogenesis mechanism, the origin, and type of these replication organelles are still a major focus of debate. To identify the cellular membranes that SARS-CoV-2 uses to generate ROs we used multiple cell lines and primary cells that were evaluated by fluorescence microscopy, genetic engineering, compounds that specifically inhibit cellular processes, and immunoprecipitation assays to validate protein-protein interactions. We also used RT-qPCR to assess viral genome replication. Result(s): SARS-CoV-2 uses the viral protein NSP6 to remodel endosomal membranes juxtaposed to the ER to generate replication organelles. Specifically, the virus depends on Clathrin, COPB1, and Rab5 for efficient SARSCoV- 2 RNA synthesis. Conclusion(s): Uncovering the origins and mechanism(s) by which SARS-CoV-2 assembles ROs opens new avenues to develop strategies to interfere with RO biogenesis and halt virus replication.

Neem and Turmeric in the management of Covid Associated Mucormycosis (CAM) derived through network pharmacology.

Mucormycosis or 'Black Fungus' has been known to target immunocompromised individuals even before the emergence of COVID-19. Nevertheless, the present circumstances provide the best opening for Covid Associated Mucormycosis (CAM), as the global pandemic is engulfing a large part of human population making them immunocompromised. This drastic increase in Mucormycosis infections has to be addressed as early as possible. There is a growing tendency of relying upon herbal drugs that have minimal side effects and does not compromise our immune system. Recently, the concept of network pharmacology has grabbed the attention of modern science, especially advanced medical sciences. This is a new discipline that can use computational power to systematically catalogue the molecular interactions between botanical formulations and the human body. In this study, Neem and Turmeric was considered as the target plants and an attempt was made to reveal various aspects through which phytocompounds derived from them may effectively manage CAM menace. We have taken a step-by-step approach for identifying the target proteins and ligands associated with Mucormycosis treatment. Functional network analysis and Molecular docking approaches were applied to validate our findings. Quercetin derived from both Neem and Turmeric was found to be one of the main phytocompounds working against Mucormycosis. Along with that, Caffeic acid, Curcumin, Kaempferol, Tetrahydrocurcumin and Myricetin also play a pivotal role in fighting against Black-Fungus. A thorough analysis of our result suggested a triple-front attack on the fungal pathogens and the approaches are necrosis inhibition, iron chelation and immuno-boosting.Communicated by Ramaswamy H. Sarma.

Exploration of Potential Biomarkers and Mechanisms for COVID-19 and Asthma Based on Microarray Analysis

Aims: The aim of this study is to investigate the potential mechanisms of coronavirus disease (COVID-19) and asthma comor-bidities.Methods: GSE147507 and GSE143303 datasets were obtained from the Gene Expression Omnibus (GEO) database, the differ-ential expressed genes (DEGs) were identified, and the overlapping DEGs were obtained by determining the DEG intersection between the two datasets. A series of analyses of the shared DEGs were performed, including enrichment analysis, protein -protein interaction (PPI) network construction, construction of transcription factor (TF)/microRNA (miRNA)-gene interaction networks, drug-gene and disease-gene interactions, and receiver operating characteristic curve (ROC) analysis.Results: A total of 135 overlapping DEGs were obtained by determining the DEGs intersection between the GSE147507 and GSE143303 datasets. These overlapped DEGs were significantly enriched in the regulation of DNA-templated transcription, initi-ation, clathrin-sculpted gamma-aminobutyric acid transport vesicle, DNA binding, and eight KEGG (kyoto encyclopedia of genes and genomes) pathways. The PPI network revealed that HSPA8, SRSF1, NDUFAB1, PTEN, CCT8, HIST1H2BK, HIST2H2BE, DLAT, EIF3G, and WAC, with high scores, were the hub genes. In addition, 65 TFs (transcription factors) and 369 miRNAs tar-geted overlapping DEGs. Finally, these overlapped DEGs were also related to other diseases, such as hyperglycemia, metabolic acidosis, and lung neoplasm, and the top 10 drugs with the most significant potential included lanatoside C, digoxin, GW-8510, doxorubicin, daunorubicin, proscillaridin, anisomycin, helveticoside, ouabain, and bisacodyl. The ROC analysis results shown that these hub genes had good diagnostic performance.Conclusions: HSPA8, SRSF1, NDUFAB1, PTEN, CCT8, HIST1H2BK, HIST2H2BE, DLAT, EIF3G, WAC, FOXC1, GATA2, hsa-miR-93-5p, and hsa-miR-17-5p may play vital roles in COVID-19 (corona virus disease-2019)/asthma comorbidity. Lanatoside C, digoxin, GW-8510, doxorubicin, daunorubicin, proscillaridin, anisomycin, helveticoside, ouabain, and bisacodyl may serve as drug targets against COVID-19/asthma comorbidity.

A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.

Protein-protein interaction is one of the ways viruses interact with their hosts. Therefore, identifying protein interactions between viruses and hosts helps explain how virus proteins work, how they replicate, and how they cause disease. SARS-CoV-2 is a new type of virus that emerged from the coronavirus family in 2019 and caused a worldwide pandemic. Detection of human proteins interacting with this novel virus strain plays an important role in monitoring the cellular process of virus-associated infection. Within the scope of the study, a natural language processing-based collective learning method is proposed for the prediction of potential SARS-CoV-2-human PPIs. Protein language models were obtained with the prediction-based word2Vec and doc2Vec embedding methods and the frequency-based tf-idf method. Known interactions were represented by proposed language models and traditional feature extraction methods (conjoint triad and repeat pattern), and their performances were compared. The interaction data were trained with support vector machine, artificial neural network (ANN), k-nearest neighbor (KNN), naive Bayes (NB), decision tree (DT), and ensemble algorithms. Experimental results show that protein language models are a promising protein representation method for protein-protein interaction prediction. The term frequency-inverse document frequency-based language model performed the SARS-CoV-2 protein-protein interaction estimation with an error of 1.4%. Additionally, the decisions of high-performing learning models for different feature extraction methods were combined with a collective voting approach to make new interaction predictions. For 10,000 human proteins, 285 new potential interactions were predicted, with models combining decisions.

Host-pathogen protein-protein interactions and interactomics in COVID-19

The current pandemic is already a third coronavirus spillover in the span of 20years. This recurrency meant that there was some information available about person-to-person transmission, cell entry, and host-pathogen interactions. The choice to target the Spike viral structural protein was easy, given its importance and specificity, together with a relatively low mutational rate. However, early in the pandemic, before any vaccines were ready, the scientific community was concerned with a lack of effective treatments against COVID-19. As soon as the viral genome was posted online, the race to identify potential host and viral targets gained pace, especially in the form of in silico "multi-omics.” Protein-protein interaction networks help identify key hub genes and proteins, as well as existing approved drugs whose mechanisms of action might be useful against this novel threat. To this end, several approaches have been devised: using influenza virus as a proxy;comparing SARS, MERS, and a common cold coronavirus to SARS-CoV-2;gene ontology matching;and the search for vulnerable proteins (VPs) and their perturbators. The algorithms deployed to perform the task have included a random walk with restart (RWR), degree of centrality, enrichment analysis, and weighted networks, backed up by experimental methods: cloning, tagging, and expressing viral proteins, affinity purification mass spectrometry, biotin labeling, and time-based transcriptomics, among others. Some of the proposed proteins and their genes reveal important pathways. Existing drugs available for repurposing encompass some interesting mechanisms of action: mRNA inhibitors, regulators of sigma receptors, and protease inhibitors. Candidate meds need to be further evaluated in clinical trials. Interestingly, most approaches give different results, and some of the insights and drugs do not make much sense. These studies, however, already make for a wealth of data to be revisited during gene discovery and drug repurposing. © 2023 Elsevier Inc. All rights reserved.

Hypothesis of a potential antiviral drug

The approval of mRNA vaccine technique against COVID-19 opens a door to research and the creation of new drugs against different infectious pathologies or even cancer, since for several diseases the therapeutic options are limited, and different viral diseases are treated only symptomatically. For these reasons, this study proposed a hypothesis supported by biological studies, that it provides a theoretical basis for the possible development of a drug that used the mRNA technique and the ribonucleolytic action of a ribonuclease for a possible antiviral therapy, and analyzed a future perspective of this technique in order to provide a bibliographic basis on this hypothesis and motivate researchers to carry out biological studies on this topic.Copyright © 2022, Health Biotechnology and Biopharma. All rights reserved.

In Silico and In Vivo Studies of a Tumor-Penetrating and Interfering Peptide with Antitumoral Effect on Xenograft Models of Breast Cancer.

The combination of a tumor-penetrating peptide (TPP) with a peptide able to interfere with a given protein-protein interaction (IP) is a promising strategy with potential clinical application. Little is known about the impact of fusing a TPP with an IP, both in terms of internalization and functional effect. Here, we analyze these aspects in the context of breast cancer, targeting PP2A/SET interaction, using both in silico and in vivo approaches. Our results support the fact that state-of-the-art deep learning approaches developed for protein-peptide interaction modeling can reliably identify good candidate poses for the IP-TPP in interaction with the Neuropilin-1 receptor. The association of the IP with the TPP does not seem to affect the ability of the TPP to bind to Neuropilin-1. Molecular simulation results suggest that peptide IP-GG-LinTT1 in a cleaved form interacts with Neuropilin-1 in a more stable manner and has a more helical secondary structure than the cleaved IP-GG-iRGD. Surprisingly, in silico investigations also suggest that the non-cleaved TPPs can bind the Neuropilin-1 in a stable manner. The in vivo results using xenografts models show that both bifunctional peptides resulting from the combination of the IP and either LinTT1 or iRGD are effective against tumoral growth. The peptide iRGD-IP shows the highest stability to serum proteases degradation while having the same antitumoral effect as Lin TT1-IP, which is more sensitive to proteases degradation. Our results support the development of the TPP-IP strategy as therapeutic peptides against cancer.

Reverse tracking from drug-induced transcriptomes through multilayer molecular networks reveals hidden drug targets.

Identifying molecular targets of a drug is an essential process for drug discovery and development. The recent in-silico approaches are usually based on the structure information of chemicals and proteins. However, 3D structure information is hard to obtain and machine-learning methods using 2D structure suffer from data imbalance problem. Here, we present a reverse tracking method from genes to target proteins using drug-perturbed gene transcriptional profiles and multilayer molecular networks. We scored how well the protein explains gene expression changes perturbed by a drug. We validated the protein scores of our method in predicting known targets of drugs. Our method performs better than other methods using the gene transcriptional profiles and shows the ability to suggest the molecular mechanism of drugs. Furthermore, our method has the potential to predict targets for objects that do not have rigid structural information, such as coronavirus.

Investigation of shared genes and regulatory mechanisms associated with coronavirus disease 2019 and ischemic stroke.

Objective: Clinical associations between coronavirus disease (COVID-19) and ischemic stroke (IS) have been reported. This study aimed to investigate the shared genes between COVID-19 and IS and explore their regulatory mechanisms. Methods: Published datasets for COVID-19 and IS were downloaded. Common differentially expressed genes (DEGs) in the two diseases were identified, followed by protein-protein interaction (PPI) network analysis. Moreover, overlapping module genes associated with the two diseases were investigated using weighted correlation network analysis (WGCNA). Through intersection analysis of PPI cluster genes and overlapping module genes, hub-shared genes associated with the two diseases were obtained, followed by functional enrichment analysis and external dataset validation. Moreover, the upstream miRNAs and transcription factors (TFs) of the hub-shared genes were predicted. Results: A total of 91 common DEGs were identified from the clusters of the PPI network, and 129 overlapping module genes were screened using WGCNA. Based on further intersection analysis, four hub-shared genes in IS and COVID-19 were identified, including PDE5A, ITGB3, CEACAM8, and BPI. These hub-shared genes were remarkably enriched in pathways such as ECM-receptor interaction and focal adhesion pathways. Moreover, ITGB3, PDE5A, and CEACAM8 were targeted by 53, 32, and 3 miRNAs, respectively, and these miRNAs were also enriched in the aforementioned pathways. Furthermore, TFs, such as lactoferrin, demonstrated a stronger predicted correlation with the hub-shared genes. Conclusion: The four identified hub-shared genes may participate in crucial mechanisms underlying both COVID-19 and IS and may exhibit the potential to be biomarkers or therapeutic targets for the two diseases.

The pathogenetic influence of smoking on SARS-CoV-2 infection: Integrative transcriptome and regulomics analysis of lung epithelial cells.

Corona virus disease (COVID-19) has been emerged as pandemic infectious disease. The recent epidemiological data suggest that the smokers are more vulnerable to infection with COVID-19; however, the influence of smoking (SMK) on the COVID-19 infected patients and the mortality is not known yet. In this study, we aimed to discern the influence of SMK on COVID-19 infected patients utilizing the transcriptomics data of COVID-19 infected lung epithelial cells and transcriptomics data smoking matched with controls from lung epithelial cells. The bioinformatics based analysis revealed the molecular insights into the level of transcriptional changes and pathways which are important to identify the impact of smoking on COVID-19 infection and prevalence. We compared differentially expressed genes (DEGs) between COVID-19 and SMK and 59 DEGs were identified as consistently dysregulated at transcriptomics levels. The correlation network analyses were constructed for these common genes using WGCNA R package to see the relationship among these genes. Integration of DEGs with network analysis (protein-protein interaction) showed the presence of 9 hub proteins as key so called "candidate hub proteins" overlapped between COVID-19 patients and SMK. The Gene Ontology and pathways analysis demonstrated the enrichment of inflammatory pathway such as IL-17 signaling pathway, Interleukin-6 signaling, TNF signaling pathway and MAPK1/MAPK3 signaling pathways that might be the therapeutic targets in COVID-19 for smoking persons. The identified genes, pathways, hubs genes, and their regulators might be considered for establishment of key genes and drug targets for SMK and COVID-19.

Determining human-coronavirus protein-protein interaction using machine intelligence.

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus -19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

COVID-19 Pandemic: An Overview of its Origin, Current Status, and Ongoing Clinical Trials

The COVID-19 pandemic is raging across the globe, with the total active cases increas-ing each day. Globally over 63 million COVID-19cases and more than 1.4 million deaths have been reported to WHO. Throughout the world, academicians, clinicians and scientists are working tirelessly on developing a treatment to combat this pandemic. The origin of novel SARS-CoV-2 virus still remains foggy but is believed to have originated from a bat coronavirus RaTG13 with which it shares approximately 96% sequence similarity. In the present review, the authors have pro-vided an overview of the COVID-19 pandemic, epidemiology, transmission, developments related to diagnosis, drugs and vaccines, along with the genetic diversity and lifecycle of the SARS-CoV-2 based on the current studies and information available.Copyright © 2022 Bentham Science Publishers.

Quercetin potentially suppresses renal fibrosis in patients with COVID-19 under hypoxic state

Introduction: Renal fibrosis is a main outcome of acute kidney injury in COVID-19 survivors, which is emerging as a global public health concern. Lung damage in the COVID-19 patients leads to acute and chronic hypoxia, which results in inflammation, epithelial-mesenchymal transformation, and fibrosis in kidney. Quercetin is an abundant flavonoid in plant materials. Previous studies indicate that quercetin alleviates the decline of renal function, suppress epithelial to mesenchymal transformation in renal tubules, and reduce fibrosis. The study aimed to explore potential targets of quercetin on treating renal fibrosis in patients with COVID-19-induced hoxpia. Method(s): Gene/protein targets related to COVID-19, renal fibrosis, or quercetin were searched from ten databases, and Cytoscape 3.8.2 was then used to construct the protein-protein interaction network and to identify the core targets. The Metascape platform was used for bioconcentration analysis, while AutoDock Vina was used as the primary molecular docking tool. In vitro, the combination model of hypoxia- and transforming growth factor-beta (TGF-beta)- treated human proximal tubule epithelial cells (HK2 cells) was applied to determine the reno-protective effect of quercetin. Result(s): The network analysis showed that quercetin targeted on TGF-beta pathway in treating COVID-19 induced renal fibrosis. In the intersection PPI network, 115 targets were obtained, and gene enrichment analysis was conducted on 109 key nodes. Molecular docking analysis revealed that quercetin could spontaneously bind to eight targets on the TGF-beta pathway, and the binding energy of TGF-beta1 was 29.82 kJ/mol. The in vitro experiment further showed that quercetin significantly suppressed fibrosis in TGF-beta and hypoxia treated HK2 cells in a dose dependent manner by inhibiting TGF-beta/Smad3 pathway. Conclusion(s): Quercetin could attenuate renal fibrosis in patients with COVID-19 by suppressing TGF-beta/Smad3 pathway. No conflict of interestCopyright © 2023

Computational Docking Study of the Phytochemical Constituent, Silybin (Silybum marianum) against SARS-CoV-2 Omicron Variant Spike Glycoprotein: An In-silico Approach

SARS-CoV-2 is continually evolving with the emergence of new variants with increased viral pathogenicity. The emergence of heavily mutated Omicron (B.1.1.529) with spike protein mutations are known to mediate its higher transmissibility and immune escape that has brought newer challenges for global public health to contain SARS-CoV-2 infection. One has to come up with a therapeutic strategy against the virus so as to effectively contain the infection and spread. Natural phytochemicals are being considered a significant source of bioactive compounds possessing an antiviral therapeutic potential. Being a promising anticancer and chemo-preventive agent, Silybin holds a significant potential to be used as a therapeutic. In the present study, molecular docking of Silybin with Omicron spike protein (7QNW) was carried out. Molecular docking results showed greater stability of Silybin in the active site of the Omicron spike protein with suitable binding mode of interactions. The study reveals that Silybin has the potential to block the host ACE2 receptor-viral spike protein binding;thereby inhibiting the viral entry to human cells. Therefore, Silybin may be further developed as a medication with the ability to effectively combat SARS-CoV-2 Omicron.Copyright © The Author(s) 2023.