Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

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.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles.

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

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.

Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans.

Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.

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.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

IR-ER- A Hybrid Pipeline for Classifying COVID-19 RNA Seq Data

Bioinformatics has numerous approaches for evaluating the similarities between RNA-seq data for disease classification. Processing RNA-sequencing (RNA-seq) data using clustering or classification approach is extremely challenging, although analysis of ribonucleic acid (RNA-Seq) helps understand differentially expressed genes and classify the patient in a risk-free method. In this study, we present a hybrid end-to-end pipeline for analyzing, processing, and classifying the RNA-Seq data with a major focus on the covid-19 data set. The pipeline has been developed in three phases initially the raw data is normalized. Then the normalized data is pushed to a colonization algorithm to remove the noise data. The optimized data set is passed to a Deep Learning (DL) classifier. Further, a comparative analysis is performed with state of art methods discussed in the literature. The results prove that our proposed hybrid pipeline achieved the best accuracy over other methods. Gene set enrichment analysis was also performed to analyze the genes that are informative towards COVID-19 identification. © 2023 ACM.

FYN, SARS-CoV-2, and IFITM3 in the neurobiology of Alzheimer's disease

Introduction: (IFITM3) is an innate immune protein that has been identified as a novel gamma-secretase (gammas) modulator. FYN is a kinase that stabilizes IFITM3 on the membrane, primes APP for amyloidogenic gammas processing and mediates tau oligomerization. The purpose of this study is to explore the role of FYN and IFITM3 in AD and COVID-19, expanding on previous research from our group. Method(s): A 520 gene signature containing FYN and IFITM3 (termed Ia) was extracted from a previously published meta-analysis of Alzheimer's disease (AD) bulk- and single nuclei sequencing data. Exploratory analyses involved meta-analysis of bulk and single cell RNA data for IFITM3 and FYN differential expression per CNS site and cellular type. Confirmatory analyses, gene set enrichment analysis (GSEA) on Ia was performed to detect overlapping enriched biological networks between COVID-19 with AD. Result(s): Bulk RNA data analysis revealed that IFITM3 and FYN were overexpressed in two CNS regions in AD vs. Controls: the temporal cortex Wilcoxon p-value=1.3e-6) and the parahippocampal cortex Wilcoxon p-value=0.012). Correspondingly, single cell RNA analysis of IFITM3 and FYN revealed that it was differentially expressed in neurons, glial and endothelial cells donated b AD patients, when compared to controls. Discussion(s): IFITM3 and FYN were found as interactors within biological networks overlapping between AD and SARS-CoV-2 infection. Within the context of SARS-CoV-2 induced tau aggregation and interactions between tau and Ab1-42, the FYN - IFITM3 regulome may outline an important innate immunity element responsive to viral infection and IFN-I signaling in both AD and COVID-19.Copyright © 2021 The Authors

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.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data.

BACKGROUND: Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS: We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks.

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

Study on the Active Compounds in Yinlian Jiedu Decoction Treating COVID-19 Based on Network Pharmacology and Molecular Docking

Objective: To explore the potential components and mechanism of Yinlian jiedu decoction in the treatment of COVID-19. Method(s): The blood components in the formula of Yinlian jiedu decoction or compounds conforming to drug-like parameters were selected as the research objects.The components that meet the requirements in Lonicerae Japonicae Flos, Forsythiae Fructus, Bupleuri Radix, Scutellariae Radix, aboveground part of Agastache rugosa, Saposhnikoviae Radix, Menthae Haplocalycis Herba, Bombyx Batryticatus, Belamcandae Rhizoma, Platycodonis Radix, Aurantii Fructus, Fritillariae Thunbergii Bulbus, Phragmitis Rhizoma, fried Stemonae Radix, Eriobotryae Folium, Citri Reticulatae Pericarpium, Astragali Radix, Codonopsis Radix, fried Atrictylodis Macrocephalac Rhizoma, Coicis Semen, Salviae Miltiorrhizae Radix et Rhizoma, Chuanxiong Rhizama, Chebulae Fructus, Glycyrrhizae Radix et Rhizoma were searched and predicted through multiple network pharmacological data platforms.The Perl command was used to batch retrieve the upstream gene name of the prediction target in the UniProt database.The target genes were brought into the ClueGO software for GO function enrichment analysis, to explore the core metabolic pathways and signal pathways and clarify the mechanism of the treatment of COVID-19 with Yinlian jiedu decoction. Result(s): The compounds-targets network consisted of 309 compounds and 1 016 corresponding targets.The key targets involved MMP1, FASN, MPO, MMP3, NQO1, MMP12, ALOX5, PTGS2, GCLM, MMP2, EGFR, GSTP1, MET, ACEII, etc.There were 238 GO items in GO functional enrichment analysis(P<0.05), including 202 biological processes(BP), 9 cellular components(CC)and 27 molecular functions(MF).The results of molecular docking showed that puerarin had the best affinity with COVID-19. Conclusion(s): Puerarin in Yinlian jiedu decoction has a direct effect on ACEII.At the same time, multiple components of Yinlian jiedu decoction play a regulatory role in multiple pathways related to respiratory diseases by acting on multiple related targets. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.

Study on the Potential Pharmacological Mechanisms of Bupleuri Radix and Puerariae Lobatae Radix Herb Pairs in Preventing and Treating Corona Virus Disease 2019 by Network Pharmacology Method

Objective: To explore the potential action mechanism of Bupleuri Radix and Puerariae Lobatae Radix herb-pair in the treatment of Corona Virus Disease 2019(COVID-19)caused by 2019-nCoV virus through network pharmacology method, so as to provide theoretical guidance for further mechanism and clinical translational research. Method(s): The potential active ingredients and their respective related action targets of Bupleuri Radix and Puerariae Lobatae Radix were obtained through the TCMSP, and the COVID-19-related disease targets were searched by GeneCards database.The common targets from the both screenings were input into the STRING protein interaction online database to construct the interaction network of potential targets.The potential core targets were further screened by MCODE plug-ins.The "ingredients-targets-diseases" network and PPI network were constructed by Cytoscape 3.2.1 software, and GO function enrichment analysis and KEGG signal pathway enrichment analysis were carried out by DAVID v6.8 online software. Result(s): A total of 22 active ingredients and 226 drug targets were screened from Bupleuri Radix and Puerariae Lobatae Radix herb-pair, among which 47 were co-acting targets with COVID-19 and 21 were potential core targets.The results of GO functional enrichment analysis showed that it was mainly related to gene transcriptional expression, inflammatory response and immune system response, while KEGG signal pathway enrichment analysis showed that it was mainly associated with influenza A virus, TNF pathway, Toll-like receptor signal pathway and others. Conclusion(s): Bupleuri Radix and Puerariae Lobatae Radix herb-pair can exert an anti-2019-nCoV effect through its regulatory role in inflammatory reaction and immune system with a multi-ingredients, multi-targets and multi-pathways pharmacological characteristics. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.

Network Pharmacology Discussion on the Mechanism of Shufeng Jiedu Granules in Treating Corona Virus Disease 2019

Objective: To study the mechanism of Shufeng jiedu granules in treating Corona Virus Disease 2019(COVID-19)based on network pharmacology. Method(s): TCMSP database was used to search and screen the active components of Shufeng jiedu granules, GeneGards database was used to predict and screen disease targets, the common targets of the above two were input into the STRING database to obtain the target protein interaction network, the PPI network and the "traditional Chinese medicines-components-targets-diseases" network were constructed by using Cytoscape 3.7.2 software, and the GO function enrichment analysis and KEGG pathway enrichment analysis were carried out by using Cytoscape 3.7.2 software, R software and the corresponding program package. Result(s): A total of 207 active components and 1 006 traditional Chinese medicine component targets were screened, and 350 COVID-19-related targets were identified, so as to obtain 49 common drug-disease targets.GO functional enrichment analysis resulted in 1 575 items(P<0.05), KEGG enrichment analysis resulted in 120 related signaling pathways(P<0.05), mainly involving IL-17 signaling pathway, TNF signaling pathway, etc. Conclusion(s): Shufeng jiedu granules may achieve the therapeutic effect of COVID-19 through multi-targets, multi-pathways to regulate virus and inflammation-related pathways. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.

Differential expression of platelet microRNAs in hospitalized patients with severe COVID-19

Background: Coronavirus disease 2019 (COVID-19) is often associated with enhanced platelet activation and thrombotic complications which may be reflected by altered microRNA (miRNA) levels in blood cells. Aim(s): We here investigated the expression of miRNAs in the background of platelet reactivity in severe COVID-19 and evaluated them for the prediction of disease mortality. Method(s): Platelet miRNAs were isolated from leukocyte-depleted platelets and profiled in COVID-19 survivors and non-survivors vs healthy controls using TaqMan Open Array. Candidate miRNAs showing at least 2-fold alteration between two groups were validated by RT-qPCR in 10 individuals from each group, and these data were correlated with clinical outcome in COVID-19. Differentially regulated miRNAs were bioinformatically analyzed to identify their mRNA targets and Cytoscape ClueGO was used for functional enrichment analysis. Platelet reactivity was evaluated via quantification of P-selectin expression, platelet-leukocyte aggregates and platelet-derived microparticles (PMPs) by flow cytometry. Result(s): Increased platelet activation was detected via elevated level of P-selectin positivity (6.5 vs 1.1%, P < 0.0001), platelet-monocyte aggregates (40 vs 17 %, P = 0.0315) and PMPs (22 vs 18 PMPs/10

Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.

The rapid spread of COVID-19 has become a major concern for people's lives and health all around the world. COVID-19 patients in various phases and severity require individualized treatment given that different patients may develop different symptoms. We employed machine learning methods to discover biomarkers that may accurately classify COVID-19 in various disease states and severities in this study. The blood gene expression profiles from 50 COVID-19 patients without intensive care, 50 COVID-19 patients with intensive care, 10 non-COVID-19 individuals without intensive care, and 16 non-COVID-19 individuals with intensive care were analyzed. Boruta was first used to remove irrelevant gene features in the expression profiles, and then, the minimum redundancy maximum relevance was applied to sort the remaining features. The generated feature-ranked list was fed into the incremental feature selection method to discover the essential genes and build powerful classifiers. The molecular mechanism of some biomarker genes was addressed using recent studies, and biological functions enriched by essential genes were examined. Our findings imply that genes including UBE2C, PCLAF, CDK1, CCNB1, MND1, APOBEC3G, TRAF3IP3, CD48, and GZMA play key roles in defining the different states and severity of COVID-19. Thus, a new point of reference is provided for understanding the disease's etiology and facilitating a precise therapy.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19.

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.