LitCovid-sentences  

PMC:7247521 / 6981-13867 JSONTXT 12 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T43 0-24 Sentence denotes 2 Materials and methods
T44 26-47 Sentence denotes 2.1 Data preparation
T45 48-240 Sentence denotes Compounds of the main herb in formula MSXG, SGMH, XCH, WLS and Others were searched in TCMSP [13], and screened based on drug-likeness (DL) ≥0.18 [14] and oral bioavailability (OB) ≥30 % [15].
T46 241-341 Sentence denotes Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16].
T47 342-537 Sentence denotes Finally, BATMAN-TCM [17], an bioinformatics analysis tool for studying TCM’s molecular mechanisms, was used to identify potential target genes of the active components (uploaded by Pubchem CIDs).
T48 538-618 Sentence denotes To make the results more credible, we set the cutoff score ≥ 30 as the standard.
T49 619-809 Sentence denotes Finally, to discovery the co-differentially presented targets in the five formulae, we conducted pan-formula analysis using Venn diagrams (http://bioinformatics.psb.ugent.be/webtools/Venn/).
T50 811-874 Sentence denotes 2.2 Functional and pathway enrichment analyses of QFPD targets
T51 875-1152 Sentence denotes To better understand the functional involvements of MSXG, SGMH, XCH, WLS and Others targets, bioinformatics analyses of multiple formulae targets were first performed, including Gene Ontology (GO) function term, KEGG biological pathway and OMIM/TTD disease enrichment analyses.
T52 1153-1451 Sentence denotes Then, kinase, microRNA and transcriptional factor (TF) enrichment analyses of the five formulae targets were conducted using the tool WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) [18] and the bubble and chord plot map were drawn with the R language ggplot2 and GOplot installation package.
T53 1452-1529 Sentence denotes P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.
T54 1531-1590 Sentence denotes 2.3 Construction of PPI network and MCODE modules analysis
T55 1591-1734 Sentence denotes To further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network.
T56 1735-1889 Sentence denotes Specifically, the five kinds of target proteins were respectively uploaded to Metascape to build PPI networks, with the species limited to “Homo sapiens”.
T57 1890-2049 Sentence denotes Next, MCODE analysis [19], a method for finding densely connected modules in PPI networks, was carried out by Cytoscape 3.2.1 (http://www.cytoscape.org/) [20].
T58 2050-2273 Sentence denotes Finally, KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway enrichment analysis was further conducted on the identified functional modules of MSXG, SGMH, XCH, WLS and Others targets PPI networks, respectively.
T59 2275-2300 Sentence denotes 2.4 Network construction
T60 2301-2464 Sentence denotes Based on the five formulae’s active components, BATMAN-TCM was used to set up five networks of components-target-pathway-disease (MSXG, SGMH, XCH, WLS and Others).
T61 2465-2705 Sentence denotes To emphasize the important elements of the five networks, we only exhibited the hub targets according to the default criteria (targets with no fewer than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively).
T62 2706-2828 Sentence denotes Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.
T63 2830-2885 Sentence denotes 2.5 ADMET evaluation of the predicted active compounds
T64 2886-3119 Sentence denotes Based on the SwissADME database [21], the physicochemical properties of the active components was predicted, including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations.
T65 3120-3616 Sentence denotes Second, pharmacokinetic properties was predicted through pkCSM database [22], which contained the absorption (Caco-2 cell permeability, HIA and skin permeability), distribution (VDss, unbound fraction, blood-brain barrier and central nervous system permeability), excretion (total clearance and renal OCT2 substrate) and toxicity (AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity).
T66 3618-3640 Sentence denotes 2.6 Molecular docking
T67 3641-3857 Sentence denotes To facilitate drug discovery against COVID-19, we used COVID-19 Docking Server (https://ncov.schanglab.org.cn/index.php) [23] to predict the binding modes between 12 COVID-19 targets and the 20 lead-likeness of QFPD.
T68 3858-4402 Sentence denotes Specifically, the 10 nonstructural and 2 structural proteins of 2019-nCov were collected (Mpro, PLpro, nsp12 [RdRp with RNA], nsp12 [RdRp without RNA], nsp13 [Helicase ADP site], nsp13 [Helicase NCB site], nsp14 [ExoN], nsp14 [N7-MTase], nsp15 [endoribonuclease], nsp16 [2′-O-MTase], N protein NCB site and E protein [ion channel]); and the corresponding Protein Data Bank (PDB)codes were 6LU7, 4OW0, 3H5Y (with RNA), 3H5Y (without RNA), 6JYT (ADP site), 6JYT (NCB site), 5C8S (ExoN),5C8S (N7-MTase), 2RHB, 2XYR, 4KYJ, and 5 × 29, respectively.
T69 4403-4528 Sentence denotes Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.
T70 4530-4599 Sentence denotes 2.7 ACE2 and CD147 expression across tissues and co-expression genes
T71 4600-4751 Sentence denotes To understand the expression and distribution of ACE2 and CD147 across tissues, a radar plot including 53 tissues was performed through COXPRESdb [24].
T72 4752-4846 Sentence denotes And the top 200 co-expression genes of ACE2 and CD147 (P < 1E-16) were obtained, respectively.
T73 4847-4986 Sentence denotes Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/).
T74 4987-5130 Sentence denotes In addition, co-expression genes of ACE2 in colonic epithelial cells [25] and HCoV-associated host proteins with references [26] were obtained.
T75 5131-5257 Sentence denotes Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.
T76 5259-5358 Sentence denotes 2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network
T77 5359-5600 Sentence denotes Since QFPD effects on COVID-19 via multi-component and multi-target, we evaluate the potential efficacy of QFPD through TCMATCOV platform, which uses the quantitative evaluation algorithm of multi-target drugs to disturb the disease network.
T78 5601-5705 Sentence denotes Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes.
T79 5706-5918 Sentence denotes The disturbance rate of drugs is calculated by comparing the changes of network topology characteristics before and after drug intervention, which is used to evaluate the intervention effect of drugs on diseases.
T80 5919-6088 Sentence denotes Firstly, COVID-19 disease network was constructed based on specific cytokines of COVID-19 [27] and differentially expressed genes of SARS (GSE36969, GSE51387, GSE68820).
T81 6089-6331 Sentence denotes Then, this platform uses four kinds of network topology characteristics to evaluate the robustness of COVID-19 network, including network average connectivity, network average shortest path, connectivity centrality and compactness centrality.
T82 6332-6482 Sentence denotes And the five formulae (MSXG, SGMH, XCH, WLS and Others) disturbance scores are calculated according to the changes before and after drug intervention.
T83 6483-6738 Sentence denotes Finally, the disturbance effect of the five formulae on the COVID-19 network was compared with null models with the total score of the disturbance, and the higher the value is, the higher the damage degree of drugs to the stability of the network is [12].
T84 6739-6886 Sentence denotes We take Banxia tianma baizhu decoction (BXTM) as negative control; and another efficient formula Yi du bi fei decoction (YDBF) as positive control.