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.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. 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.

Study on active compounds from Maxingganshi decoction for treatment of novel coronavirus pneumonia (COVID-19) based on network pharmacology and molecular docking method

Aim To explore the active compound of Maxingganshi decoction in treatment of novel coronavirus pneumonia(COVID-19). Methods With the help of TCMSP database, the chemical components and action targets of ephedra, almond, licorice, and gypsum in Maxingganshi decoction were searched, and then a C-T network, protein interaction analysis, GO functional enrichment analysis, and KEGG pathway enrichment were constructed. Analysis was performed to predict its mechanism of action. Results A total of 120 compounds in Maxingganshi decoction corresponded to 222 targets. PTGS2, ESR1, PPARG, AR, NOS2, NCOA2 acted on PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, T cell receptor signaling pathways, etc. The results of molecular docking showed that the affinity of quercetin, kaempferol, glabridin and other core compounds was similar to recommended drugs in treatment of COVID-19. Conclusions The active compounds of Maxingganshi decoction can target multiple pathways to achieve the therapeutic effect of COVID-19.Copyright © 2020 Publication Centre of Anhui Medical University. All rights reserved.

Glioblastoma Susceptibility to SARS-CoV-2: Analysis of Cancer Stem Cells and Immune Tumor Microenvironment on Human Brain, Patient-Derived Tumors, and Organoid Models

INTRODUCTION: Glioblastoma (GBM) is the most common and deadliest primary brain tumor, characterized by chemoradiation resistance and an immunosuppressive tumor microenvironment (TME). SARS-CoV-2, the COVID-19 virus, produces a significant proinflammatory response and a spectrum of clinical presentations after central nervous system infection. METHOD(S): Patient-derived GBM tissue, primary cell lines, and organoids were analyzed with immunohistochemistry and pixel-line intensity quantification. Data from tumor-bulk and single-cell transcriptomics served to describe the cell-specific expression of SARS-CoV-2 receptors in GBM and its association with the immune TME phenotype. Normal brain and iPSC-derived organoids served as controls. RESULT(S): We demonstrate that patient-derivedGBMtissue and cell cultures express SARS-CoV2 entry factors such as ACE2, TMPRSS2, and NRP1. NRP1 expression was higher in GBM than in normal brains (p<0.05), where it plays a crucial role in SARS-CoV-2 infection. NRP1 was expressed in a cell-type and phenotype-specific manner and correlated with TME infiltration of immunosuppressive cells: M2 macrophages (r = 0.229), regulatory T cells (r = 0.459), NK cells (r = -0.346), and endothelial cells (r = 0.288) (p < 0.05). Furthermore, gene ontology enrichment analysis showed that leukocyte migration and chemotaxis are among the top 5 biological functions mediated by NRP1 (p < 0.05). We found our GBM organoids recapitulate tumoral expression of SARSCoV- 2 entry factors, which varies based on distance from surface as surrogate of TME oxygenation (p < 0.05). CONCLUSION(S): GBM cancer cells and immune TME cells express SARS-CoV-2 entry factors. Glioblastoma organoids recapitulate this expression and allow for currently undergoing studies analyzing the effect of SARS-CoV-2 infection in GBM. Our findings suggest that SARSCoV- 2 could potentially target GBM, opening the door to future studies evaluating SARS-CoV-2-driven immune modulation.

In silico identification of potential miRNAs -mRNA inflammatory networks implicated in the pathogenesis of COVID-19

COVID-19 has been found to affect the expression profile of several mRNAs and miRNAs, leading to dysregulation of a number of signaling pathways, particularly those related to inflammatory responses. In the current study, a systematic biology procedure was used for the analysis of high-throughput expression data from blood specimens of COVID-19 and healthy individuals. Differentially expressed miRNAs in blood specimens of COVID-19 vs. healthy specimens were then identified to construct and analyze miRNA-mRNA networks and predict key miRNAs and genes in inflammatory pathways. Our results showed that 171 miRNAs were expressed as outliers in box plot and located in the critical areas according to our statistical analysis. Among them, 8 miRNAs, namely miR-1275, miR-4429, miR-4489, miR-6721-5p, miR-5010-5p, miR-7110-5p, miR-6804-5p and miR-6881-3p were found to affect expression of key genes in NF-KB, JAK/STAT and MAPK signaling pathways implicated in COVID-19 pathogenesis. In addition, our results predicted that 25 genes involved in above-mentioned inflammatory pathways were targeted not only by these 8 miRNAs but also by other obtained miRNAs (163 miRNAs). The results of the current in silico study represent candidate targets for further studies in COVID-19.Copyright © 2023 Elsevier B.V.

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.

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature;(2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction;(3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects. © 2022 IEEE.

Deep Graph Networks for Drug Repurposing with Multi-Protein Targets

In the early phases of the COVID-19 pandemic, repurposing of drugs approved for use in other diseases helped counteract the aggressiveness of the virus. Therefore, the availability of effective and flexible methodologies to speed up and prioritize the repurposing process is fundamental to tackle present and future challenges to worldwide health. This work addresses the problem of drug repurposing through the lens of deep learning for graphs, by designing an architecture that exploits both structural and biological information to propose a reduced set of drugs that may be effective against an unknown disease. Our main contribution is a method to repurpose a drug against multiple proteins, rather than the most common single-drug/single-protein setting. The method leverages graph embeddings to encode the relevant proteins'and drugs'information based on gene ontology data and structural similarities. Finally, we publicly release a comprehensive and unified data repository for graph-based analysis to foster further studies on COVID-19 and drug repurposing. We empirically validate the proposed approach in a general drug repurposing setting, showing that it generalizes better than single protein repurposing schemes. We conclude the manuscript with an exemplified application of our method to the COVID-19 use case. All source code is publicly available. IEEE

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2).

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.Copyright © 2022 Editorial Department of Journal of Shanghai Second Medical University. All rights reserved.

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Identification of phytochemical compounds of Fagopyrum dibotrys and their targets by metabolomics, network pharmacology and molecular docking studies.

Acute lung injury (ALI) is a clinically severe lung illness with high incidence rate and mortality. Especially, coronavirus disease 2019 (COVID-19) poses a serious threat to world wide governmental fitness. It has distributed to almost from corner to corner of the universe, and the situation in the prevention and control of COVID-19 remains grave. Traditional Chinese medicine plays a vital role in the precaution and therapy of sicknesses. At present, there is a lack of drugs for treating these diseases, so it is necessary to develop drugs for treating COVID-19 related ALI. Fagopyrum dibotrys (D. Don) Hara is an annual plant of the Polygonaceae family and one of the long-history used traditional medicine in China. In recent years, its rhizomes (medicinal parts) have attracted the attention of scholars at home and abroad due to their significant anti-inflammatory, antibacterial and anticancer activities. It can work on SARS-COV-2 with numerous components, targets, and pathways, and has a certain effect on coronavirus disease 2019 (COVID-19) related acute lung injury (ALI). However, there are few systematic studies on its aerial parts (including stems and leaves) and its potential therapeutic mechanism has not been studied. The phytochemical constituents of rhizome of F. dibotrys were collected using TCMSP database. And metabolites of F. dibotrys' s aerial parts were detected by metabonomics. The phytochemical targets of F. dibotrys were predicted by the PharmMapper website tool. COVID-19 and ALI-related genes were retrieved from GeneCards. Cross targets and active phytochemicals of COVID-19 and ALI related genes in F. dibotrys were enriched by gene ontology (GO) and KEGG by metscape bioinformatics tools. The interplay network entre active phytochemicals and anti COVID-19 and ALI targets was established and broke down using Cytoscape software. Discovery Studio (version 2019) was used to perform molecular docking of crux active plant chemicals with anti COVID-19 and ALI targets. We identified 1136 chemicals from the aerial parts of F. dibotrys, among which 47 were active flavonoids and phenolic chemicals. A total of 61 chemicals were searched from the rhizome of F. dibotrys, and 15 of them were active chemicals. So there are 6 commonly key active chemicals at the aerial parts and the rhizome of F. dibotrys, 89 these phytochemicals's potential targets, and 211 COVID-19 and ALI related genes. GO enrichment bespoken that F. dibotrys might be involved in influencing gene targets contained numerous biological processes, for instance, negative regulation of megakaryocyte differentiation, regulation of DNA metabolic process, which could be put down to its anti COVID-19 associated ALI effects. KEGG pathway indicated that viral carcinogenesis, spliceosome, salmonella infection, coronavirus disease - COVID-19, legionellosis and human immunodeficiency virus 1 infection pathway are the primary pathways obsessed in the anti COVID-19 associated ALI effects of F. dibotrys. Molecular docking confirmed that the 6 critical active phytochemicals of F. dibotrys, such as luteolin, (+) -epicatechin, quercetin, isorhamnetin, (+) -catechin, and (-) -catechin gallate, can combine with kernel therapeutic targets NEDD8, SRPK1, DCUN1D1, and PARP1. In vitro activity experiments showed that the total antioxidant capacity of the aerial parts and rhizomes of F. dibotrys increased with the increase of concentration in a certain range. In addition, as a whole, the antioxidant capacity of the aerial part of F. dibotrys was stronger than that of the rhizome. Our research afford cues for farther exploration of the anti COVID-19 associated ALI chemical compositions and mechanisms of F. dibotrys and afford scientific foundation for progressing modern anti COVID-19 associated ALI drugs based on phytochemicals in F. dibotrys. We also fully developed the medicinal value of F. dibotrys' s aerial parts, which can effectively avoid the waste of resources. Meanwhile, our work provides a new strategy for integrating metabonomics, network pharmacology, and molecular docking techniques which was an efficient way for recognizing effective constituents and mechanisms valid to the pharmacologic actions of traditional Chinese medicine.

Exploration of the Shared Molecular Mechanisms between COVID-19 and Neurodegenerative Diseases through Bioinformatic Analysis.

The COVID-19 pandemic has caused millions of deaths and remains a major public health burden worldwide. Previous studies found that a large number of COVID-19 patients and survivors developed neurological symptoms and might be at high risk of neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD). We aimed to explore the shared pathways between COVID-19, AD, and PD by using bioinformatic analysis to reveal potential mechanisms, which may explain the neurological symptoms and degeneration of brain that occur in COVID-19 patients, and to provide early intervention. In this study, gene expression datasets of the frontal cortex were employed to detect common differentially expressed genes (DEGs) of COVID-19, AD, and PD. A total of 52 common DEGs were then examined using functional annotation, protein-protein interaction (PPI) construction, candidate drug identification, and regulatory network analysis. We found that the involvement of the synaptic vesicle cycle and down-regulation of synapses were shared by these three diseases, suggesting that synaptic dysfunction might contribute to the onset and progress of neurodegenerative diseases caused by COVID-19. Five hub genes and one key module were obtained from the PPI network. Moreover, 5 drugs and 42 transcription factors (TFs) were also identified on the datasets. In conclusion, the results of our study provide new insights and directions for follow-up studies of the relationship between COVID-19 and neurodegenerative diseases. The hub genes and potential drugs we identified may provide promising treatment strategies to prevent COVID-19 patients from developing these disorders.

Assessment of GO-Based Protein Interaction Affinities in the Large-Scale Human-Coronavirus Family Interactome.

SARS-CoV-2 is a novel coronavirus that replicates itself via interacting with the host proteins. As a result, identifying virus and host protein-protein interactions could help researchers better understand the virus disease transmission behavior and identify possible COVID-19 drugs. The International Committee on Virus Taxonomy has determined that nCoV is genetically 89% compared to the SARS-CoV epidemic in 2003. This paper focuses on assessing the host-pathogen protein interaction affinity of the coronavirus family, having 44 different variants. In light of these considerations, a GO-semantic scoring function is provided based on Gene Ontology (GO) graphs for determining the binding affinity of any two proteins at the organism level. Based on the availability of the GO annotation of the proteins, 11 viral variants, viz., SARS-CoV-2, SARS, MERS, Bat coronavirus HKU3, Bat coronavirus Rp3/2004, Bat coronavirus HKU5, Murine coronavirus, Bovine coronavirus, Rat coronavirus, Bat coronavirus HKU4, Bat coronavirus 133/2005, are considered from 44 viral variants. The fuzzy scoring function of the entire host-pathogen network has been processed with ~180 million potential interactions generated from 19,281 host proteins and around 242 viral proteins. ~4.5 million potential level one host-pathogen interactions are computed based on the estimated interaction affinity threshold. The resulting host-pathogen interactome is also validated with state-of-the-art experimental networks. The study has also been extended further toward the drug-repurposing study by analyzing the FDA-listed COVID drugs.

Genomic regulatory network of human olfactory neuroepithelial cells infected with novel coronavirus (SARS-COV-2)

Objective To explore the genomic changes of human olfactory neuroepithelial cells after the novel coronavirus (SARS-COV-2) infecting the human body, and establish a protein-protein interaction (PPI) network of differentially expressed genes (DEGs), in order to understand the impact of SARS-COV-2 infection on human olfactory neuroepithelial cells, and provide reference for the prevention and treatment of new coronavirus pneumonia. Methods The public dataset GSE151973 was analyzed by NetworkAnalyst. DEGs were selected by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis. PPI network, DEGs-microRNA regulatory network, transcription factor-DEGs regulatory network, environmental chemicals-DEGs regulatory network, and drug-DEGs regulatory network were created and visualized by using Cytoscape 3.7.2. Results After SAR-COV-2 invading human olfactory neuroepithelial cells, part of the gene expression profile was significantly up-regulated or down-regulated. A total of 568 DEGs were found, including 550 up-regulated genes (96.8%) and 18 down-regulated genes (3.2%). DEGs were mainly involved in biological processes such as endothelial development and angiogenesis of the olfactory epithelium, and the expression of molecular functions such as the binding of the N-terminal myristylation domain. PPI network suggested that RTP1 and RTP2 were core proteins. MAZ was the most influential transcription factor. Hsa-mir-26b-5p had the most obvious interaction with DEGs regulation. Environmental chemical valproic acid and drug ethanol had the most influence on the regulation of DEG. Conclusion The gene expression of olfactory neuroepithelial cells is significantly up-regulated or down-regulated after infection with SAR-COV-2. SARS-CoV-2 may inhibit the proliferation and differentiation of muscle satellite cells by inhibiting the function of PAX7. RTP1 and RTP2 may resist SARS-CoV-2 by promoting the ability of olfactory receptors to coat the membrane and enhancing the ability of olfactory receptors to respond to odorant ligands. MAZ may regulate DEGs by affecting cell growth and proliferation. Micro RNA, environmental chemicals and drugs also play an important role in the anti-SAR-COV-2 infection process of human olfactory neuroepithelial cells.

Analysis of medication regularities of epidemic disease prescription in Treatise on Febrile Diseases and mechanism of core drugs in treatment of COVID-19

Objective To analyze the medication rules of related epidemic disease prescription in Treatise on Febrile Diseases based on data mining, and the mechanism of "Chaihu (Bupleuri Radix)-Huangqin (Scutellariae Radix)” as the core drugs in the treatment of coronavirus disease 2019 (COVID-19) by network pharmacology, in order to explore the contemporary value of classical prescriptions in the treatment of epidemic diseases. Methods The prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were screened, and the medication rules such as drug frequency, flavor and meridian tropism as well as correlation, apriori algorithm were analyzed by using software such as R language. The mechanism of the core drugs in the medication pattern in the treatment of COVID-19 was explored by the network pharmacology. A "disease-drug-ingredient-target” network was constructed on the selected components and targets with Cytoscape. The key targets were introduced into String database for network analysis of protein-protein interaction (PPI), and gene ontology (GO) functional analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were conducted in R language. Results A total of 61 prescriptions for treating epidemic diseases in Treatise on Febrile Diseases were included, including 52 traditional Chinese medicines (TCMs). In the top 20 high-frequency drugs, warm drugs, spicy drugs and qitonifying drugs were mainly used, mostly in the spleen and lung meridian. Chaihu (Bupleuri Radix) and Huangqin (Scutellariae Radix) herb pair had the strongest correlation. A total of five clusters were excavated: supplemented formula of Xiaochaihu Decoction (小柴胡汤), Sini Decoction (四逆汤), supplemented formule of Maxing Shigan Decoction (麻杏石甘汤), Fuling Baizhu Decoction (茯苓白术汤) and Dachengqi Decoction (大承气汤). A total of 45 active ingredients, 189 action targets of Bupleuri Radix-Scutellariae Radix herb pair, and 543 targets of COVID-19 were obtained from TCMSP and Genecards, and 64 intersection targets were generated. The results of the network analysis showed that the main components of core drugs pair against COVID-19 may be quercetin, wogonin, kaempferol baicalein, acacetin etc., and the core targets may be VEGFA, TNF, IL-6, TP53, AKT1, CASP3, CXCL8, PTGS2, etc. A total of 1871 related entries and 164 pathways were obtained by GO and KEGG enrichment analysis, respectively. Conclusion In Treatise on Febrile Diseases, the treatment of epidemic diseases mainly chose pungent, warm, spleen-invigorating and qi-tonifying herbs, such as Xiaochaihu Decoction, Sini Decoction and Dachengqi Decoction, etc. It was found that Bupleuri Radix-Scutellariae Radix core herb pair prevent and treat COVID-19 through multi-target targets such as PTGS2, IL-6 and TNF. The ancient prescriptions for treating epidemic disease in Treatise on Febrile Diseases may have significant reference value for the prevention and treatment of new epidemic diseases today. © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Bioinformatics and system biology approach to identify the influences of COVID-19 on primary sjogren's syndrome patients

Background: Primary Sjogren's syndrome (pSS) is a chronic, systemic, inflammatory autoimmune disease in which existing studies have found the presence of pSS-specific antibodies anti-SSA/ Ro in acute infection with COVID-19.1 The emergence of this phenomenon makes us aware that in the context of the long-term epidemic of COVID-19, it is necessary to further study the molecular mechanisms of the high susceptibility of pSS patients to COVID-19. Method(s): The gene expression profiles of 8 COVID-19 datasets and 5 pSS datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between COVID-19 and PSS were identified using the limma software package and Weighted Gene Co-expression Network Analysis (WGCNA). A Venn diagram was used to discover common upregulated DEGs. To explore the possible pathogenesis of both diseases, common signaling pathways were explored by enriching DEGs using Gene Ontology (GO) and the Kyoto Gene and Genome Encyclopedia (KEGG) pathway. Protein-protein interactions (PPIs) were established to identify hub genes and key modules. The analysis of key gene expression characteristics by The Connectivity Map was used to predict potentially effective drugs. Finally, the CIBERSORT method was used to comprehensively evaluate the immune infiltrates of patients with COVID-19 and PSS to study the mechanisms that may have a common immune response or immune cell infiltration. Result(s): A total of 82 upregulated DEGs were identified in both COVID-19 and PSS (Figure 1 A-E). Functional enrichment analysis illustrated the important role of enhanced signaling pathways in response to virus defense and interferon-alpha in both diseases (Figure 1F).Three key modules including 25 hub genes were identified (Figure 1G). The correlation analysis of immune cell infiltration showed the expression of B cells memory resting decreased and NK cells resting increased significantly in the two diseases (Figure 1H, I). Finally, estradiol in drug prediction outcomes has been shown to reduce susceptibility to COVID-19 and its severity through its involvement in regulating immune cells, while the most common manifestation of dry eye in pSS patients is strongly associated with low estrogen. Conclusion(s): High defense response to virus and response to interferon-alpha in pSS patients might be a crucial susceptible factor for COVID-19 and predictive drugs such as estradiol, suggested by susceptibility genes common to COVID-19 and pSS, may help in the clinical treatment of both diseases.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

MTOR hypermethylation may associate with the susceptibility and survival of SARS-CoV-2 infections to lung adenocarcinoma patients based on multi-omics data and machine learning

Recent studies have shown that lung adenocarcinoma (LUAD) patients have a higher risk and worse prognosis of COVID-19 caused by SARS-CoV-2 compared to normal samples. Whereas, in addition to the receptor for SARS-CoV-2, other genes also deserve attention. In our study, we identified 19 differentially methylated genes (DMGs) that were co-upregulated in LUAD and COVID-19 samples. These 19 DMGs mainly regulated the immune-related and multiple viral infection signaling pathways. Gene Ontology and pathway enrichment analysis were applied with these genes. Then, 6 key DMGs (MTOR, ACE, IGF1, PTPRC, C3, and PTGS2) were identified by constructing and analyzing the protein-protein interaction (PPI) network. Besides, MTOR was significantly associated with 5 prognostic markers (CDO1, NEURL4, SMAP1, NPEPPS, IQCK) identified by survival analysis based on machine learning. In total, MTOR hypermethylation may be related to the susceptibility of LUAD patients to SARS-CoV-2 and the prognosis of LUAD patients suffering from COVID-19. © 2022 IEEE.