LitCovid-sentences  

PMC:7247521 / 2386-48395 JSONTXT 13 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T20 0-15 Sentence denotes 1 Introduction
T21 16-129 Sentence denotes 2019-novel coronavirus (2019-nCov) outbreak took place in December 2019 and continues to spread around the world.
T22 130-243 Sentence denotes By April 3, 2020, more than 1 million patients have been diagnosed with corona virus disease 2019 (COVID-19) [1].
T23 244-459 Sentence denotes The virus has a long incubation period, is highly contagious, and is generally susceptible to all types of people, which has a huge negative impact on people's health, economic development, and social stability [2].
T24 460-551 Sentence denotes However, there is still a lack of effective clinical drugs or vaccine to control the virus.
T25 552-685 Sentence denotes Traditional Chinese medicine has a good effect on viral infectious pneumonia and has shown a certain effect in the treatment of SARS.
T26 686-1377 Sentence denotes On February 7, 2020, the China Health Commission and the Administration of Traditional Chinese Medicine jointly issued a notice recommending formula Qingfei Paidu decoction (QFPD, Herba Ephedrae, Radix Glycyrrhizae, Semen Armeniacae Amarum, Gypsum Fibrosum, Ramulus Cinnamomi, Rhizoma Alismatis, Polyporus Umbellatus, Rhizoma Atractylodis Macrocephalae, Poria, Radix Bupleuri, Radix Scutellariae, Rhizome Pinelliae Preparata, Rhizoma Zingiberis Recens, Radix Asteris, Flos Farfarae, Rhizoma Belamcandae, Herba Asari, Rhizoma Dioscoreae, Fructus Aurantii Immaturus, Pericarpium Citri Reticulatae, Herba Pogostemonis) for the treatment of COVID-19 according to clinical treatment and efficacy.
T27 1378-1634 Sentence denotes QFPD is a compound prescription in TCM including Ma Xing Shi Gan decoction (MSXG), She Gan Ma Huang decoction (SGMH), Xiao Chai Hu (XCH), and Wu Ling San (WLS), which was first discovered in the classic Treatise on Exogenous Febrile Disease (Shanghan Lun).
T28 1635-1881 Sentence denotes MXSG (Herba Ephedrae, Radix Glycyrrhizae, Semen Armeniacae Amarum, Gypsum Fibrosum) has been used for the treatment of the common cold, fever, and influenza virus infections via damaging the viral surface structure and inhibiting viral entry [3].
T29 1882-2146 Sentence denotes SGMH (Herba Ephedrae, Rhizome Pinelliae Preparata, Rhizoma Zingiberis Recens, Radix Asteris, Flos Farfarae, Rhizoma Belamcandae, Herba Asari) is a classical prescription for the treatment of flu-like symptoms, asthma, inflammation, tonsillitis and sore throat [4].
T30 2147-2332 Sentence denotes XCH (Radix Glycyrrhizae, Radix Bupleuri, Radix Scutellariae, Rhizome Pinelliae Preparata, Rhizoma Zingiberis Recens) possesses antiviral [5] and various anticarcinogenic properties [6].
T31 2333-2572 Sentence denotes WLS (Ramulus Cinnamomi, Rhizoma Alismatis, Polyporus Umbellatus, Rhizoma Atractylodis Macrocephalae, Poria), a famous Chinese prescription for nephritic syndrome, can improve kidney excretion function and inhibit inflammatory response [7].
T32 2573-2668 Sentence denotes These researches indicate that MXSG, SGMH, XCH and WLS may be functional units of formula QFPD.
T33 2669-2788 Sentence denotes Previous studies have focused on the mechanism of compound prescription based on a single traditional Chinese medicine.
T34 2789-2884 Sentence denotes However, it may not reflect functional compatibility mechanism of traditional Chinese medicine.
T35 2885-3062 Sentence denotes Therefore, it is worthy of comparing the similarities and differences of different QFPD functional units in the treatment of COVID-19, including MXSG, SGMH, XCH, WLS and Others.
T36 3063-3297 Sentence denotes QFPD contains a total of 21 traditional Chinese medicines, and it is difficult to elucidate the complex mechanism of QFPD on COVID-19 by traditional pharmacological methods due to the multi-components and multi-targets of the formula.
T37 3298-3455 Sentence denotes Network pharmacology, a new method in recent years, can integrate interactions of drugs, targets, pathways and diseases into a biological network system [8].
T38 3456-3727 Sentence denotes Therefore, more and more TCM researchers have begun to use network pharmacology to explore the material basis of TCM, and to reveal the overall comprehensive effects of multi-path, multi-component and multi-target of TCM prescription and its treatment of diseases [9,10].
T39 3728-3897 Sentence denotes More importantly, previous study reported that disease conditions can be more fragile than health systems against various perturbations for the un-optimized system [11].
T40 3898-4053 Sentence denotes So the formula may be more effective for COVID-19 disease via the stronger effects on the reduction of the robustness of the COVID-19 disease network [12].
T41 4054-4274 Sentence denotes In our study, since MSXG, SGMH, XCH and WLS have been independently used for the treatment of viral infectious pneumonia, this study firstly screened out major effective compounds from five functional units respectively.
T42 4275-4593 Sentence denotes Then we offered a new understanding of the functional units mechanism of QFPD against COVID-19 by a novel functional units of network pharmacology (FUNP) approach and formula perturbation analysis, and provided a combination strategy to explore mechanisms of inter-ingredients interactions from a holistic perspective.
T43 4595-4619 Sentence denotes 2 Materials and methods
T44 4621-4642 Sentence denotes 2.1 Data preparation
T45 4643-4835 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 4836-4936 Sentence denotes Then, the corresponding Pubchem CIDs of the compounds were retrieved from the Pubchem database [16].
T47 4937-5132 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 5133-5213 Sentence denotes To make the results more credible, we set the cutoff score ≥ 30 as the standard.
T49 5214-5404 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 5406-5469 Sentence denotes 2.2 Functional and pathway enrichment analyses of QFPD targets
T51 5470-5747 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 5748-6046 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 6047-6124 Sentence denotes P-values were adjusted for multiple testing by Benjamini-Hochberg adjustment.
T54 6126-6185 Sentence denotes 2.3 Construction of PPI network and MCODE modules analysis
T55 6186-6329 Sentence denotes To further explore the pharmacological mechanisms, five PPI networks were built including: MSXG, SGMH, XCH, WLS and Others targets PPI network.
T56 6330-6484 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 6485-6644 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 6645-6868 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 6870-6895 Sentence denotes 2.4 Network construction
T60 6896-7059 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 7060-7300 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 7301-7423 Sentence denotes Finally, these important linking compounds of MSXG, SGMH, XCH, WLS and Others networks were obtained for further analysis.
T63 7425-7480 Sentence denotes 2.5 ADMET evaluation of the predicted active compounds
T64 7481-7714 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 7715-8211 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 8213-8235 Sentence denotes 2.6 Molecular docking
T67 8236-8452 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 8453-8997 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 8998-9123 Sentence denotes Finally, Discovery Studio software elucidated the 14 best docking results between compounds and the COVID-19 target proteins.
T70 9125-9194 Sentence denotes 2.7 ACE2 and CD147 expression across tissues and co-expression genes
T71 9195-9346 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 9347-9441 Sentence denotes And the top 200 co-expression genes of ACE2 and CD147 (P < 1E-16) were obtained, respectively.
T73 9442-9581 Sentence denotes Then, text mining method from the literature was used to screen for pneumonia-associated genes through COREMINE (http://www.coremine.com/).
T74 9582-9725 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 9726-9852 Sentence denotes Finally, we performed UpsetView analysis (http://www.ehbio.com/ImageGP/) between these five sets of proteins and QFPD targets.
T76 9854-9953 Sentence denotes 2.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network
T77 9954-10195 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 10196-10300 Sentence denotes Specifically, the disturbing effect of drugs on diseases is simulated by deleting disease network nodes.
T79 10301-10513 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 10514-10683 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 10684-10926 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 10927-11077 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 11078-11333 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 11334-11481 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.
T85 11483-11492 Sentence denotes 3 Result
T86 11494-11560 Sentence denotes 3.1 Prediction of active components and potential targets of QFPD
T87 11561-11698 Sentence denotes Firstly, the DL ≥ 0.18 and OB ≥ 30 %.s were set as the standard to screen the chemical components obtained through online database TCMSP.
T88 11699-11918 Sentence denotes Specifically, a total of 175 effective components of QFPD were screened from the TCM database, including 82 species of MSXG, 35 species of SGMH, 105 species of XCH, 21 species of WLS and 32 species of Others (Table 1 ).
T89 11919-12141 Sentence denotes Among these effective components, 89 (50.86 %) components exited in more than two formulae; CID5280343 and CID5280794 were owned by MXSG, Others, SGMH and XCH; CID12303645 was owned by MXSG, Others, WLS and XCH (Fig. 1 A).
T90 12142-12342 Sentence denotes Secondly, a total of 300 targets of QFPD were screened from the BATMAN-TCM database, including 192 targets of MSXG, 201 targets of SGMH, 221 targets of XCH, 96 targets of WLS and 99 targets of Others.
T91 12343-12415 Sentence denotes Among these proteins, 21 (7%) targets exited in five formulae (Fig. 1B).
T92 12416-12453 Sentence denotes Table 1 Effective components of QFPD.
T93 12454-12475 Sentence denotes Formula N PubChem_Cid
T94 12476-13371 Sentence denotes MSXG 82 CID10090416,CID10542808,CID10881804,CID11267805,CID114829,CID11558452,CID11602329,CID11975273,CID120074,CID12303645,CID124049,CID124052,CID13965473,CID14604077,CID14604078,CID14604081,CID15228663,CID15380912,CID162412,CID177149,CID193679,CID197678,CID23724664,CID25015742,CID268208,CID336327,CID354368,CID3764,CID439246,CID440833,CID442411,CID44257530,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280448,CID5280544,CID5280794,CID5280863,CID5281619,CID5281654,CID5281789,CID5282768,CID5282805,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5320083,CID5460988,CID5481234,CID5481948,CID5481949,CID5997,CID636883,CID637112,CID64971,CID6918970,CID73205,CID928837,CID9927807,CID15840593,CID15228662
T95 13372-13858 Sentence denotes SGMH 35 CID1135,CID1174,CID3026,CID5789,CID6782,CID6998,CID8437,CID8679,CID13625,CID117158,CID159225,CID185,CID10019512,CID11438306,CID11869417,CID11870462,CID12315507,CID162350,CID16726037,CID222284,CID389888,CID3902,CID440833,CID5280343,CID5280445,CID5280544,CID5280794,CID5280863,CID5281331,CID5281605,CID5281616,CID5281628,CID5281654,CID5281779,CID5282768,CID5315890,CID5316876,CID5320945,CID5484202,CID5488781,CID5491637,CID64959,CID64982,CID676152,CID71307581,CID72307,CID13688752
T96 13859-14999 Sentence denotes XCH 105 CID10090416,CID10542808,CID10881804,CID11267805,CID11438306,CID114829,CID11558452,CID11602329,CID117443,CID12303645,CID124049,CID124052,CID124211,CID13965473,CID14135323,CID14604077,CID14604078,CID14604081,CID15228662,CID15228663,CID15380912,CID156992,CID158311,CID15840593,CID159029,CID161271,CID162412,CID177149,CID185034,CID193679,CID197678,CID222284,CID23724664,CID25015742,CID25721350,CID268208,CID336327,CID354368,CID373261,CID3764,CID389001,CID389888,CID439246,CID442411,CID44257530,CID44258628,CID480774,CID480780,CID480787,CID480859,CID480873,CID49856081,CID503731,CID503737,CID5280343,CID5280378,CID5280442,CID5280448,CID5280794,CID5280863,CID5281605,CID5281619,CID5281654,CID5281674,CID5281703,CID5281789,CID5282768,CID5312521,CID5316900,CID5317300,CID5317478,CID5317479,CID5317480,CID5317652,CID5317768,CID5317777,CID5318437,CID5318585,CID5318679,CID5318869,CID5318998,CID5318999,CID5319013,CID5319042,CID5319252,CID5320083,CID5320315,CID5320399,CID5321865,CID5322059,CID5322078,CID5460988,CID5481234,CID5481948,CID5481949,CID5484202,CID636883,CID637112,CID64959,CID64971,CID64982,CID73205,CID821279,CID928837,CID9927807
T97 15000-15240 Sentence denotes WLS 21 CID10181133,CID10743008,CID12303645,CID14036811,CID15225964,CID15226717,CID15976101,CID182232,CID222284,CID44575602,CID5283628,CID5471851,CID5471852,CID56668247,CID6436630,CID712316,CID73402,CID9064,CID9805290,CID14448075,CID14236575
T98 15241-15585 Sentence denotes Others 32 CID10212,CID11824478,CID122159,CID12303645,CID14057197,CID145659,CID17897,CID33934,CID373261,CID40429858,CID42607889,CID439246,CID442834,CID443024,CID5280343,CID5280445,CID5280794,CID5281326,CID5281617,CID5281781,CID5319406,CID5320621,CID5495928,CID5997,CID631170,CID632135,CID676152,CID712316,CID72344,CID79730,CID1149877,CID45359875
T99 15586-15657 Sentence denotes Fig. 1 Venn diagram of the five formulae’ active compounds and targets.
T100 15658-15683 Sentence denotes A: compounds, B: targets.
T101 15685-15748 Sentence denotes 3.2 Functional and pathway enrichment analyses of QFPD targets
T102 15749-16021 Sentence denotes As shown in Fig. 2 , the 11 enriched GO terms of the targets in all five formulae were found, such as oxidoreductase activity, lipid metabolic process, lipid binding, small molecule metabolic process, homeostatic process, signal transducer activity and cell proliferation.
T103 16022-16392 Sentence denotes Furthermore, the results of pathway enrichment analysis showed that the 7 KEGG pathways were significantly related to more than 4 formula groups, including dteroid biosynthesis, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, steroid hormone biosynthesis, PPAR signaling pathway, arginine and proline metabolism and ABC transporters (Fig. 3 A).
T104 16393-16623 Sentence denotes In addition, TTD analysis showed that the 8 diseases were significantly association with more than 3 formula groups, such as acne, Behcet'S disease, benign prostate hyperplasia, intrahepatic cholestasis and brain injury (Fig. 3B).
T105 16624-16733 Sentence denotes However, the five formulae contained their specific (MSXG, SGMH, XCH, WLS and Others) GO, KEGG and TTD terms.
T106 16734-17127 Sentence denotes For example, neurological system process and beta-Alanine metabolism terms were specific for MXSG; membrane organization and parasitic infections of the eye terms for SGMH; circadian rhythm and hypertension terms for XCH; nucleic acid binding transcription factor activity, ovarian steroidogenesis and chronic inflammatory diseases terms for WLS; fat digestion and absorption terms for Others.
T107 17128-17196 Sentence denotes Fig. 2 Bubble plot of the GO analysis of the five formulae’ targets.
T108 17197-17271 Sentence denotes Fig. 3 Bubble plot of the KEGG/TTD analysis of the five formulae’ targets.
T109 17272-17288 Sentence denotes A: KEGG, B: TTD.
T110 17289-17494 Sentence denotes In the prediction of miRNAs in QFPD targets, MIR-183 and MIR-130A/B/301 were the highest linking terms to bind the five formulae targets and formulae Others was the highest group to bind miRNAs (Fig. 4 A).
T111 17495-17622 Sentence denotes In addition, kinase prediction revealed CDK7 were significantly enriched in formulae MSXG, SGMH, XCH, WLS and Others (Fig. 4B).
T112 17623-17782 Sentence denotes Finally, TF analysis showed that LXR was the highest linking TF to bind the four formulae targets and formulae WLS was the highest group to bind TFs (Fig. 4C).
T113 17783-17868 Sentence denotes Fig. 4 The miRNA, kinase and TF analysis of the five formulae’ targets by WebGestalt.
T114 17869-17978 Sentence denotes Chord plot showing the five formulae’ targets present in the represented enriched miRNA, kinase and TF terms.
T115 17979-18084 Sentence denotes Outer ring shows miRNA/kinase/TF term and log2 enrichment ratio (left) or five formulae grouping (right).
T116 18085-18142 Sentence denotes Chords connect miRNA/kinase/TF term with formulae groups.
T117 18143-18170 Sentence denotes A: miRNA, B: kinase, C: TF.
T118 18172-18231 Sentence denotes 3.3 Construction of PPI network and MCODE modules analysis
T119 18232-18397 Sentence denotes To further explore the functional relationship among five formulae, PPI networks were constructed through Metascape, and visual composition carried out by Cytoscape.
T120 18398-18603 Sentence denotes Firstly, the potential 192 target genes of MXSG were analyzed by PPI network, and the results showed that there were 144 nodes and 510 edges, which represented the interaction between protein and function.
T121 18604-18748 Sentence denotes The MXSG PPI network function module was confirmed by the MCODE plug-in and a list of the corresponding meaningful modules presented (Fig. 5 A).
T122 18749-18777 Sentence denotes 3 modules scores were > 2.5.
T123 18778-18794 Sentence denotes Module 1 (score:
T124 18795-18870 Sentence denotes 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score:
T125 18871-18947 Sentence denotes 4.429) consisted of 14 nodes and the seed gene was ALDH1A1; module 3 (score:
T126 18948-19002 Sentence denotes 5.0) consisted of 11 nodes and the seed gene was CNR2.
T127 19003-19202 Sentence denotes KEGG pathway enrichment analysis showed that MXSG modules were enriched in neuroactive ligand-receptor interaction, calcium signaling pathway, inflammatory mediator regulation of TRP channels, et.al.
T128 19203-19241 Sentence denotes Fig. 5 KEGG analysis of MCODE modules.
T129 19242-19384 Sentence denotes MCODE analysis was performed after the construction of the five formulae’ targets PPI; then, KEGG analysis was conducted on the MCODE modules.
T130 19385-19396 Sentence denotes A: MXSG, B:
T131 19397-19429 Sentence denotes Others, C: WLS, D: SGMH, E: XCH.
T132 19430-19572 Sentence denotes Secondly, the potential 99 target genes of Others were analyzed by PPI network, and the results showed that there were 77 nodes and 194 edges.
T133 19573-19614 Sentence denotes Only 1 module score were > 2.5 (Fig. 5B).
T134 19615-19631 Sentence denotes Module 1 (score:
T135 19632-19690 Sentence denotes 5.769) consisted of 13 nodes and the seed gene was COX7A1.
T136 19691-19851 Sentence denotes KEGG pathway enrichment analysis showed that Others modules were enriched in huntington's disease, glycolysis / gluconeogenesis, Notch signaling pathway, et.al.
T137 19852-19990 Sentence denotes Thirdly, the potential 96 target genes of WLS were analyzed by PPI network, and the results showed that there were 60 nodes and 143 edges.
T138 19991-20033 Sentence denotes Only 1 modules score were > 2.5 (Fig. 5C).
T139 20034-20050 Sentence denotes Module 1 (score:
T140 20051-20107 Sentence denotes 2.706) consisted of 17 nodes and the seed gene was CNR2.
T141 20108-20297 Sentence denotes KEGG pathway enrichment analysis showed that WLS modules were enriched in thyroid hormone signaling pathway, adipocytokine signaling pathway, neuroactive ligand-receptor interaction, et.al.
T142 20298-20440 Sentence denotes Fourthly, the potential 201 target genes of SGMH were analyzed by PPI network, and the results showed that there were 153 nodes and 505 edges.
T143 20441-20479 Sentence denotes 3 modules scores were > 2.5 (Fig. 5D).
T144 20480-20496 Sentence denotes Module 1 (score:
T145 20497-20571 Sentence denotes 3.529) consisted of 17 nodes and the seed gene was ACSS1; Module 2 (score:
T146 20572-20642 Sentence denotes 4.5) consisted of 7 nodes and the seed gene was CNR2; module 3 (score:
T147 20643-20697 Sentence denotes 3.5) consisted of 8 nodes and the seed gene was PRKCG.
T148 20698-20859 Sentence denotes KEGG pathway enrichment analysis showed that SGMH modules were enriched in insulin resistance, adipocytokine signaling pathway, Th17 cell differentiation, et.al.
T149 20860-21000 Sentence denotes At last, the potential 221 target genes of XCH were analyzed by PPI network, and the results showed that there were 166 nodes and 643 edges.
T150 21001-21039 Sentence denotes 5 modules scores were > 2.5 (Fig. 5E).
T151 21040-21056 Sentence denotes Module 1 (score:
T152 21057-21132 Sentence denotes 5.769) consisted of 13 nodes and the seed gene was COX7A1; Module 2 (score:
T153 21133-21206 Sentence denotes 2.769) consisted of 13 nodes and the seed gene was RRM1; module 3 (score:
T154 21207-21278 Sentence denotes 5.5) consisted of 12 nodes and the seed gene was CNR2; module 4 (score:
T155 21279-21353 Sentence denotes 2.909) consisted of 11 nodes and the seed gene was ACSS1; module 5 (score:
T156 21354-21408 Sentence denotes 3.0) consisted of 7 nodes and the seed gene was FFAR1.
T157 21409-21585 Sentence denotes KEGG pathway enrichment analysis showed that XCH modules were enriched in calcium signaling pathway, cGMP-PKG signaling pathway, neuroactive ligand-receptor interaction, et.al.
T158 21587-21612 Sentence denotes 3.4 Network construction
T159 21613-21748 Sentence denotes After using the BATMAN-TCM, we constructed five ingredients-target-pathway-disease networks, including MSXG, SGMH, XCH, WLS and Others.
T160 21749-21965 Sentence denotes In order to emphasize the important network elements, we showed the networks that exhibit those targets with larger than 6, 5, 8, 7 and 4 linking compounds for MSXG, SGMH, XCH, WLS and Others, respectively (Fig. 6 ).
T161 21966-22316 Sentence denotes MSXG network contained 31 key components, 50 proteins and 17 pathways; SGMH network contained 15 key components, 20 proteins and 8 pathways; WLS network contained 18 key components, 9 proteins and 4 pathways; XCH network contained 32 key components, 15 proteins and 12 pathways; Others network contained 10 key components, 13 proteins and 3 pathways.
T162 22317-22434 Sentence denotes To find the potential drugs of formulae QFPD for COVID-19, a total of 67 hub components were used for ADMET analysis.
T163 22435-22487 Sentence denotes Fig. 6 The component-target-pathway-disease network.
T164 22488-22504 Sentence denotes Purple polygons:
T165 22505-22607 Sentence denotes PubChem ID of QFPD compounds; blue pentagrams: QFPD targets; yellow circles: KEGG pathway; red square:
T166 22608-22669 Sentence denotes Therapeutic Target Database (TTD) disease term, green square:
T167 22670-22726 Sentence denotes Online Mendelian Inheritance in Man (OMIN) disease term.
T168 22727-22763 Sentence denotes A: MXSG, B: SGMH, C: WLS, D: XCH, E:
T169 22764-22902 Sentence denotes Others. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T170 22904-22949 Sentence denotes 3.5 ADMET evaluation of the 67 key compounds
T171 22950-23105 Sentence denotes Since in silico ADMET prediction can help early drug design and evaluation, ADMET properties of the 67 key compounds were predicted by SwissADME and pkCSM.
T172 23106-23300 Sentence denotes Chemical properties including molecular weight (MW), rotatable bonds count, H-bond acceptors and donors count, TPSA and leadlikeness violations were calculated by SwissADME and shown as Fig. 8A.
T173 23301-23520 Sentence denotes It is worth mentioning that 21 (31.34 %) compounds passed the stringent lead-like criteria (250 g/mol ≤ MW ≤ 350 g/mol, XLOGP ≤ 3.5 and rotatable bonds ≤ 7), which are excellent candidates for drug discovery (Fig. 7 A).
T174 23521-23646 Sentence denotes And these lead-likeness compounds were further predicted by pkCSM, with the exception of S3 (low gastrointestinal absorption)
T175 23647-23719 Sentence denotes Fig. 7 Chemical properties statistics of hub components in the formulae.
T176 23720-23722 Sentence denotes A:
T177 23723-24023 Sentence denotes Molecular weight, B: rotatable bond count, C: H-bond acceptors count, D: H-bond donors count, E: topological polar surface area (TPSA), F: leadlikeness violations, G: pharmacokinetic and toxicity evaluated parameters of 20 leadlikeness compounds by pkCSM; green = good, yellow = tolerable, red = bad.
T178 24024-24030 Sentence denotes Caco2:
T179 24031-24055 Sentence denotes Caco-2 Permeability,HIA:
T180 24056-24092 Sentence denotes Intestinal Absorption (Human), Skin:
T181 24093-24145 Sentence denotes Skin Permeability, VDss: volume of distribution, FU:
T182 24146-24176 Sentence denotes Fraction Unbound (Human), BBB:
T183 24177-24215 Sentence denotes Blood Brain Barrier permeability, CNS:
T184 24216-24255 Sentence denotes Central Nervous System permeability,TC:
T185 24256-24278 Sentence denotes Total Clearance, OCT2:
T186 24279-24341 Sentence denotes Renal Organic Cation Transporter 2, AMES: AMES toxicity, MTDD:
T187 24342-24416 Sentence denotes Maximum Tolerated Dose (Human), hERG I/II: hERG I and II Inhibitors, LD50:
T188 24417-24452 Sentence denotes Oral Rat Acute Toxicity (LD50), HT:
T189 24453-24472 Sentence denotes Hepatotoxicity, SS:
T190 24473-24496 Sentence denotes Skin Sensitisation, MT:
T191 24497-24644 Sentence denotes Minnow toxicity. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T192 24645-24772 Sentence denotes Fig. 8 Schematic (3D and 2D) representation that molecular model of specific compounds of each formulae with COVID-19 proteins.
T193 24773-24930 Sentence denotes A: M3 and E protein [ion channel], B: M3 and nsp13 [Helicase NCB site], C: S1 and nsp13 [Helicase ADP site], D: S1 and PLpro, E: X2 and Mpro, F: O2 and Mpro.
T194 24931-24959 Sentence denotes M: MXSG, S: SGMH, X: XCH, O:
T195 24960-24967 Sentence denotes Others.
T196 24968-25147 Sentence denotes Regarding the absorption parameters, all 20 compounds (Table 2 ) presented a promising oral availability including the optimal Caco-2 cell permeability, HIA and skin permeability.
T197 25148-25379 Sentence denotes The drug distribution results showed that most of the compounds distributed in tissue (VDss> 0.45: tissue, VDss <−0.15: plasma) with good unbound fraction scores, thus becoming available to interact with the pharmacological target.
T198 25380-25502 Sentence denotes Only compound W5 and W11 were entirely unable to penetrate the blood-brain barrier (BBB) and central nervous system (CNS).
T199 25503-25637 Sentence denotes In addition, 15 compounds presented a good renal elimination and were not substrates of the renal organic cation transporter 2 (OCT2).
T200 25638-25893 Sentence denotes Finally, 14 compounds did not present any particular toxicity problems including AMES toxicity, maximum tolerated dose, hERG I inhibitor, hERG II inhibitor, oral rat acute toxicity (LD50), hepatotoxicity, skin sensitisation, and minnow toxicity (Fig. 7B).
T201 25894-25942 Sentence denotes Table 2 20 potential active compounds from QFPD.
T202 25943-26008 Sentence denotes Pubchem Molecular Name Structure Pubchem Molecular Name Structure
T203 26009-26069 Sentence denotes CID6918970 M3 ZINC5356864 CID10019512 S5 3-O-Methylviolanone
T204 26070-26115 Sentence denotes CID336327 M5 Medicarpin CID9064 W5 Cianidanol
T205 26116-26162 Sentence denotes CID14057197 O1 – CID182232 W11 (+)-Epicatechin
T206 26163-26219 Sentence denotes CID42607889 O2 Alysifolinone CID25721350 X1 ZINC13130930
T207 26220-26277 Sentence denotes CID3902 S1 letrozole CID14135323 X2 (2S)-dihydrobaicalein
T208 26278-26327 Sentence denotes CID821279 X4 ZINC338038 CID439246 MXO1 naringenin
T209 26328-26387 Sentence denotes CID440833 MS1 Leucocyanidol CID676152 SO1 SR-01,000,767,148
T210 26388-26450 Sentence denotes CID177149 MX16 (+)-Vestitol CID11438306 SX1 cyclo(L-Tyr-l-Phe)
T211 26451-26508 Sentence denotes CID114829 MX17 Liquiritigenin CID712316 WO1 (-)-taxifolin
T212 26509-26571 Sentence denotes CID928837 MX8 ZINC519174 CID373261 XO1 Eriodyctiol (flavanone)
T213 26572-26600 Sentence denotes M: MXSG, S: SGMH, X: XCH, O:
T214 26601-26608 Sentence denotes Others.
T215 26610-26632 Sentence denotes 3.6 Molecular docking
T216 26633-27158 Sentence denotes The application of COVID-19 docking server and Discovery Studio software elucidated the interactions between the 20 lead-likeness compounds (S1, W5, MX17, MX16, W11, M5, XO1, MXO1, SO1, WO1, X4, MX8, M3, S5, SX1, O1, X2, X1, O2, MS1) and the 10 nonstructural and 2 structural proteins (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, E protein [ion channel]).
T217 27159-27213 Sentence denotes The docking scores were depicted in Table 3, Table 4 .
T218 27214-27358 Sentence denotes The smaller of docking score, the lower of energy would be required, which means the binding between the compounds and the targets are stronger.
T219 27359-27436 Sentence denotes There are 9 compounds presenting better bonding ability than other compounds.
T220 27437-27519 Sentence denotes Table 3 Docking score between specific ingredients of QFPD and 2019-nCov proteins.
T221 27520-27562 Sentence denotes Molecule M3 M5 S1 S5 W11 W5 X1 X2 X4 O1 O2
T222 27563-27631 Sentence denotes Main Protease −7.7 −7.3 −6.8 −6.9 −7.2 −7.5 −7.1 −7.9 −7.3 −7.1 −7.4
T223 27632-27703 Sentence denotes Papain-like protease −8.7 −8.5 −9.9 −7.7 −8 −8 −8.4 −8.7 −8.2 −8.6 −8.2
T224 27704-27772 Sentence denotes RdRp with RNA −8.4 −8.5 −8.5 −8.3 −9.1 −9.1 −8.4 −8.2 −8.2 −8.3 −8.6
T225 27773-27844 Sentence denotes RdRp without RNA −6.8 −6.8 −6.9 −6.8 −6.7 −6.9 −7.1 −7.2 −6.5 −7.3 −7.1
T226 27845-27913 Sentence denotes Helicase ADP site −6.3 −6.3 −7.3 −6.5 −6 −6 −6.3 −6.5 −6.2 −6.1 −6.5
T227 27914-27986 Sentence denotes Helicase NCB site −7.9 −6.9 −7.2 −7.1 −7.2 −7.2 −7.4 −7.5 −7.3 −7.4 −7.4
T228 27987-28053 Sentence denotes Nsp14(ExoN) −6.8 −6.6 −6.3 −6.4 −6.9 −6.8 −6.7 −6.9 −6.4 −6.6 −6.9
T229 28054-28122 Sentence denotes Nsp14(N7-MTase) −8.8 −8.1 −8.5 −7.5 −8.4 −8.4 −8.3 −8.5 −8 −8.3 −8.5
T230 28123-28201 Sentence denotes Nsp15(endoribonuclease) −6.6 −6.3 −6.3 −5.9 −6.2 −6.2 −6.2 −6.3 −6.2 −6.4 −6.4
T231 28202-28274 Sentence denotes Nsp16(2′-O-MTase) −7.5 −7.4 −7.5 −7.2 −8.2 −8.2 −7.7 −7.9 −7.7 −7.9 −8.4
T232 28275-28344 Sentence denotes N protein NCB site −7.6 −7.5 −7.8 −7.6 −7.6 −7.6 −8 −8 −7.5 −7.6 −7.6
T233 28345-28420 Sentence denotes E protein(ion channel) −8.1 −7 −7.8 −6.7 −6.4 −6.4 −7.2 −7.3 −7.2 −7.1 −6.8
T234 28421-28449 Sentence denotes M: MXSG, S: SGMH, X: XCH, O:
T235 28450-28457 Sentence denotes Others.
T236 28458-28549 Sentence denotes Table 4 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network.
T237 28550-28593 Sentence denotes Topology MXSG SGMH XCH WLS Others BXTM YDBF
T238 28594-28639 Sentence denotes DTS 25.66 26.71 21.02 17.64 23.16 14.52 22.71
T239 28640-28684 Sentence denotes AC −4.63 −5.21 −3.02 −3.49 −5.38 −2.32 −3.78
T240 28685-28727 Sentence denotes APL 13.35 13.40 9.96 6.49 10.66 4.59 11.17
T241 28728-28773 Sentence denotes CoC −1.44 −1.64 −1.59 −1.30 −1.23 −1.15 −1.25
T242 28774-28819 Sentence denotes ClC −6.24 −6.46 −6.45 −6.36 −5.88 −6.46 −6.52
T243 28820-29005 Sentence denotes Average connectivity (AC), Connection centrality (CoC), Closeness centrality (ClC): the larger the quotient is, the more stable the network and, the less the influence made by the drug.
T244 29006-29181 Sentence denotes Disturbance total score (DTS), Average length of shortest path (APL), : the larger the quotient is, the less stable the network and, the larger the influence made by the drug.
T245 29182-29213 Sentence denotes Negative control formula: BXTM.
T246 29214-29245 Sentence denotes Positive control formula: YDBF.
T247 29246-29661 Sentence denotes M3 (Fig. 8A), a specific compound in formulae MXSG, showed eight interactions with E protein [ion channel] including Pi-sigma, Pi-alkyl and Alkyl, which were connected with TYR 57, ALA 32, ILE 46 and PRO 54, etc.; additionally, M3 (Fig. 8B) showed five interactions with nsp13 [Helicase NCB site] including Unfavorable Donor-Donor, Pi-alkyl and Alkyl, which were connected with ASN 559, ARG 409, LEU 42 and PRO 406.
T248 29662-30110 Sentence denotes S1 (Fig. 8C), a specific compound in formulae SGMH, showed seven interactions with nsp13 [Helicase ADP site] including H-bond interactions, van der waals, Amide-Pi stacked and Pi-alkyl, which were connected with ALA 313, ASP 374, GLN 537 and SER 289, etc.; additionally, S1 (Fig. 8D) showed five interactions with PLpro including Pi-anion, Pi-Pi stacked, Pi-Pi T-shaped and Pi-alkyl, which were connected with TYR 264, ASP 164, TYR 268 and PRO 248.
T249 30111-30335 Sentence denotes X2 (Fig. 8E), a specific compound in formulae XCH, showed seven interactions with Mpro including H-bond interactions, Pi-Donor hydrogen bond and Pi-alkyl, which were connected with MET 165, GLU 166, LEU 141 and CYS 145, etc.
T250 30336-30581 Sentence denotes O2 (Fig. 8F), a specific compound in formulae Others, showed seven interactions with Mpro including H-bond interactions, Carbon hydrogen bond, Pi-anion, Pi-sulfur and Pi-alkyl, which were connected with MET 131, GLY 71, LEU 100 and CYS 115, etc.
T251 30582-31035 Sentence denotes MS1 (Fig. 9 A), a compound in formulae MXSG and SGMH, showed eleven interactions with N protein NCB site including H-bond interactions, Pi-Donor hydrogen bond, Pi-sigma, Pi-Pi stacked and Pi-alkyl, which were connected with SER 51, THR 109, ALA 50 and PRO 42, etc.; additionally, MS1 (Fig. 9B) showed five interactions with nsp14 [ExoN] including H-bond interactions and Pi-Pi stacked, which were connected with GLU 92, PHE 190, ASP 273 and VAL 91, etc.
T252 31036-31265 Sentence denotes MX16 (Fig. 9C), a compound in formulae MXSG and XCH, showed seven interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with PRO 343, VAL 275, LYS 344 and SER 293, etc.
T253 31266-31635 Sentence denotes SX1 (Fig. 9D), a compound in formulae SGMH and XCH, showed two interactions with nsp14 [N7-MTase] including Pi-Pi stacked and Pi-alkyl, which were connected with PHE 426; additionally, SX1 (Fig. 9E) showed five interactions with nsp15 [endoribonuclease] including H-bond interactions, Alkyl and Pi-alkyl, which were connected with LYS 344, LYS 289, VAL 291 and PRO 343.
T254 31636-32156 Sentence denotes WO1 (Fig. 9F), a compound in formulae WLS and Others, showed seven interactions with nsp16 [2′-O-MTase] including H-bond interactions, Carbon hydrogen bond, Pi-Pi T-shaped, Pi-alkyl and Pi-anion, which were connected with PHE 149, CYS 115, ASP 99 and SER 74, etc.; additionally, WO1 (Fig. 9G) showed seven interactions with nsp12 [RdRp without RNA] including H-bond interactions, Carbon hydrogen bond, Unfavorable Donor-Donor, Pi-cation and Pi-anion, which were connected with THR 556, ARG 553, ASP 623 and SER 682, etc.
T255 32157-32415 Sentence denotes XO1 (Fig. 9H), a compound in formulae XCH and Others, showed ten interactions with nsp12 [RdRp with RNA] including H-bond interactions, Pi-Donor hydrogen bond, Pi-Pi T-shaped and Pi-alkyl, which were connected with CYS 813, GLY 590, LYS 593 and ASP 865, etc.
T256 32416-32545 Sentence denotes Fig. 9 Schematic (3D and 2D) representation that molecular model of common compounds of the five formulae with COVID-19 proteins.
T257 32546-32807 Sentence denotes A: MS1 and N protein NCB site, B: MS1 and nsp14 [ExoN], C: MX16 and nsp15 [endoribonuclease], D: SX1 and nsp14 [N7-MTase], E: SX1 and nsp15 [endoribonuclease], F: WO1 and nsp16 [2′-O-MTase], G: WO1 and nsp12 [RdRp without RNA], H: XO1 and nsp12 [RdRp with RNA].
T258 32808-32902 Sentence denotes MS: MXSG and SGMH, MX: MXSG and XCH, SX: SGMH and XCH, WO: WLS and Others, XO: XCH and Others.
T259 32904-32973 Sentence denotes 3.7 ACE2 and CD147 expression across tissues and co-expression genes
T260 32974-33145 Sentence denotes Since 2019-nCov may enter other tissues and organs through ACE2 and CD147 binding, we firstly explored the expression and distribution of ACE2 and CD147 across 53 tissues.
T261 33146-33329 Sentence denotes Fig. 10 A showed that the 5 top expression tissues of ACE2 were terminal ileum, testis, visceral (omentum), left ventricle and kidney cortex, which are 3 fold change higher than lung.
T262 33330-33473 Sentence denotes And the 5 top expression tissues of CD147 were testis, visceral (omentum), left ventricle, aorta, atrial appendage and transformed fibroblasts.
T263 33474-33765 Sentence denotes Then, to further understand whether QFPD only targets pneumonia or 2019-nCov, we obtained 200 co-expression genes of ACE2, 200 co-expression genes of CD147, 470 pneumonia-associated proteins, 119 HCoV-associated host proteins, and 476 co-expression genes of ACE2 in colonic epithelial cells.
T264 33766-33996 Sentence denotes Fig. 10B displayed that QFPD had some common targets with these five sets, while specific 254 targets for QFPD, indicating other mechanisms of QFPD on COVID-19 in addition to 2019-nCov, pneumonia, ACE2 and CD147 related functions.
T265 33997-34070 Sentence denotes Fig. 10 ACE2 and CD147 expression across tissues and co-expression genes.
T266 34071-34073 Sentence denotes A:
T267 34074-34132 Sentence denotes Radar plot of ACE2 and CD147 expression across 53 tissues.
T268 34133-34190 Sentence denotes The expression values were converted to base-2 logarithm.
T269 34191-34249 Sentence denotes Red triangle and square mean the top 5 expression tissues.
T270 34250-34252 Sentence denotes B:
T271 34253-34478 Sentence denotes UpSet plot of proteins among QFPD, HCoV (Host_protein), pneumonia, ACE2 co-expression genes (ACE2_database), CD147 co-expression genes (CD147_database), and ACE2 co-expression genes in colonic epithelial cells (ACE2_colonic).
T272 34479-34577 Sentence denotes The horizontal bar graph at the bottom left shows the total number of proteins for each group set.
T273 34578-34667 Sentence denotes Circles and vertical lines in the x-axis mark the corresponding data sets being compared.
T274 34668-34887 Sentence denotes The vertical bar graph at the top quantitates the number of proteins in the comparisons. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T275 34889-34988 Sentence denotes 3.8 Validation of drug positioning for QFPD against COVID-19 via the robustness of disease network
T276 34989-35123 Sentence denotes Firstly, the robustness of whole networks against formula attack was assessed to evaluate QFPD attack on the COVID-19 disease network.
T277 35124-35402 Sentence denotes Interestingly, Table 4 and Fig. 11 showed that MSXG, SGMH, XCH, WLS and Others attack on the COVID-19 network were characterized by greater disturbance score than negative control (BXTM), and increasing dependence on hub nodes, indicating greater fragility under formula attack.
T278 35403-35514 Sentence denotes In addition, SGMH, MSXG, and Others exerted higher disturbance score than the positive control (YDBF) Table 5 .
T279 35515-35606 Sentence denotes Fig. 11 Evaluation of the effect of QFPD on the robustness disturbance of COVID-19 network.
T280 35607-35712 Sentence denotes Blue normal distribution: drug attack on random networks as a null distribution for the permutation test.
T281 35713-35793 Sentence denotes Red vertical line: the disturbance rate of the drug to the real disease network.
T282 35794-35871 Sentence denotes Fist row: MXSG, second row: SGMH, third row: XCH, fourth row: WLS, fifth row:
T283 35872-35879 Sentence denotes Others.
T284 35880-36158 Sentence denotes Fist column: average connectivity, second row: average length of shortest path, third row: connection centrality, fourth row: closeness centrality. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T285 36159-36239 Sentence denotes Table 5 Docking score between common ingredients of QFPD and 2019-nCov proteins.
T286 36240-36287 Sentence denotes Molecule MS1 MX16 MX17 MX8 MXO1 SO1 SX1 WO1 XO1
T287 36288-36344 Sentence denotes Main Protease −7.6 −7 −7.8 −7.8 −7.8 −7.2 −7.1 −7.4 −7.4
T288 36345-36408 Sentence denotes Papain-like protease −8 −8.2 −8.1 −8.1 −8.2 −8.2 −9.2 −8.7 −8.3
T289 36409-36463 Sentence denotes RdRp with RNA −8.6 −8 −7.9 −7.9 −8 −8.4 −8.2 −8.8 −9.5
T290 36464-36523 Sentence denotes RdRp without RNA −7.1 −6.8 −6.8 −6.8 −7 −7.1 −6.9 −7.5 −7.1
T291 36524-36586 Sentence denotes Helicase ADP site −6.2 −6.5 −6.3 −6.1 −6.2 −6.4 −6.8 −6.2 −6.8
T292 36587-36649 Sentence denotes Helicase NCB site −7.4 −7.1 −7.2 −7.2 −7.5 −7.5 −7.4 −7.6 −7.5
T293 36650-36704 Sentence denotes Nsp14(ExoN) −7.2 −6.6 −6.6 −6.7 −6.9 −7 −6.9 −7.1 −7.1
T294 36705-36765 Sentence denotes Nsp14(N7-MTase) −8.6 −8.3 −8.4 −8.3 −8.6 −8.3 −9.4 −8.7 −8.7
T295 36766-36834 Sentence denotes Nsp15(endoribonuclease) −6.2 −6.8 −6.4 −6.4 −6.3 −6.5 −6.8 −6.4 −6.6
T296 36835-36897 Sentence denotes Nsp16(2′-O-MTase) −8.1 −7.4 −7.6 −7.8 −7.8 −8.3 −7.7 −8.4 −8.2
T297 36898-36957 Sentence denotes N protein NCB site −8.1 −7.9 −8 −8 −7.6 −7.8 −7.5 −7.7 −7.9
T298 36958-37025 Sentence denotes E protein(ion channel) −6.4 −6.9 −7.2 −7.2 −6.9 −6.9 −7.9 −6.8 −6.8
T299 37026-37054 Sentence denotes M: MXSG, S: SGMH, X: XCH, O:
T300 37055-37062 Sentence denotes Others.
T301 37063-37197 Sentence denotes Next, to illustrate the mechanism of QFPD against COVID-19, a formula-attacked target-KEGG pathway network was constructed (Fig. 12 ).
T302 37198-37551 Sentence denotes This network showed that MSXG, SGMH, XCH, WLS and Others interacted with 8 drug-attacked nodes (Cdc20, Ido1, Ifng, Il10, Il6, Ptger4, Spi1, Tnf), and 24 drug-attacked nodes in the COVID-19 network were related to Graft-versus-host disease, cytokine-cytokine receptor interaction, asthma, influenza A, inflammatory bowel disease, JAK-STAT signaling, etc.
T303 37552-37610 Sentence denotes Fig. 12 Disturbance analysis of QFPD for COVID-19 network.
T304 37611-37613 Sentence denotes A:
T305 37614-37666 Sentence denotes Venn diagram of the five formulae’ attacked targets.
T306 37667-37669 Sentence denotes B:
T307 37670-37798 Sentence denotes Formula-attacked target-KEGG pathway network; green square: formula, yellow diamond: attacked target, red circle: KEGG pathways.
T308 37799-37992 Sentence denotes The bigger the size of the nodes is, the higher the degree is. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
T309 37994-38007 Sentence denotes 4 Discussion
T310 38008-38098 Sentence denotes Novel coronavirus (2019-nCov) infection is characterized by lung and immune system damage.
T311 38099-38220 Sentence denotes Severe infection can lead to acute respiratory distress syndrome (ARDS) and septicemia, and eventually lead to death [2].
T312 38221-38305 Sentence denotes In addition, a number of patients presented multi-organ damage and dysfunction [28].
T313 38306-38388 Sentence denotes However, there are no specific drugs or vaccine for the treatment of the COVID-19.
T314 38389-38511 Sentence denotes This reason may partly be that a single targeted drug cannot cure a complex disease with complex biological networks [29].
T315 38512-38733 Sentence denotes Despite the lack of strong evidence-based medicine, TCM has a good potential to complement the medical service for COVID-19, including reverting radiological changes, and shortening fever duration and hospital stay. [30].
T316 38734-38879 Sentence denotes It is observed that the total effective rate of QFPD in the treatment of pneumonia patients infected by novel coronavirus is more than 90 % [31].
T317 38880-39071 Sentence denotes Therefore, we explored the mechanism of QFPD against COVID-19 by systems pharmacology, and provided a combination strategy to explore the functional units in QFPD from a holistic perspective.
T318 39072-39190 Sentence denotes To our knowledge, this is the first study to explore the mechanisms of QFPD for COVID based on intra-functional units.
T319 39191-39535 Sentence denotes In this study, GO enrichment analysis showed that the common GO terms of MSXG, SGMH, XCH, WLS and Others targets were significant enriched in oxidoreductase activity, lipid metabolic process, lipid binding, small molecule metabolic process, and homeostatic process etc., suggesting QFPD may exert anti-viral activity through metabolic function.
T320 39536-39752 Sentence denotes In agreement with these results, a recent research has found that lipid metabolic reprograming plays an important role in virus replication, which may be an appealing and applicable target for antiviral therapy [32].
T321 39753-39985 Sentence denotes KEGG analysis showed that in addition to lipid metabolism-related pathways, endocrine system pathways were also significantly enriched in more than four formulae, including PPAR signaling pathway and adipocytokine signaling pathway.
T322 39986-40218 Sentence denotes A recent study has showed that the host can exert anti-inflammatory functions to inhibit excessive inflammatory damage through PPAR signaling pathway after H1N1 infection, thus keeping homeostasis of metabolism and development [33].
T323 40219-40524 Sentence denotes In addition, other common terms were significant enriched in more than two formulae, such as immune system process, endoplasmic reticulum, cell-cell signaling, calcium signaling pathway, vascular smooth muscle contraction, inflammatory mediator regulation of TRP channels, cardiac muscle contraction, etc.
T324 40525-40727 Sentence denotes Therefore, the multi-pathway and multi-target results of our intra functional unit of QFPD not only showed a new useful method for studying TCM, but may demonstrate the rationality of TCM compatibility.
T325 40728-40791 Sentence denotes Moreover, TCMATCOV platform was used to validate these results.
T326 40792-41194 Sentence denotes Interestingly, all the five FUs of QFPD showed higher disturbance score than negative control (BXTM), indicating that MSXG, SGMH, XCH, WLS and Others may protect COVID-19 independently, and target 8 specifically expressed drug-attacked nodes (Cdc20, Ido1, Ifng, Il10, Il6, Ptger4, Spi1, Tnf) which were related to the bacterial and viral responses, cytokine, immune system, signaling transduction, etc.
T327 41195-41449 Sentence denotes Currently, a number of studies have showed that 2019-nCov can cause multiple organs dysfunction, including liver [34], pancreas [35], kidney [36], throat and rectum [37], which may be the reason that a wide distribution of ACE2 across these tissues [38].
T328 41450-41721 Sentence denotes In agreement with these results, we found that ACE2 was highly expressed in terminal ileum, testis, adipose visceral omentum, heart left ventricle, kidney cortex and thyroid, etc., and QFPD has only 15.33 % common targets with 2019-nCov, pneumonia and ACE2 related genes.
T329 41722-41831 Sentence denotes These results indicate that the effective treatment of QFPD for COVID-19 may be through a holistic treatment.
T330 41832-42114 Sentence denotes Moreover, TTD analysis further displayed that QFPD targets were significantly enriched in many COVID-19 related disease, such as chronic inflammatory diseases, asthma, inflammatory bowel disease, chronic obstructive pulmonary disease, intrahepatic cholestasis, chronic ileitis, etc.
T331 42115-42265 Sentence denotes It is known that ADMETox prediction is an important part in evaluating if a drug can be toxic or can be absorbed during drug development process [39].
T332 42266-42505 Sentence denotes In our study, ADMETox evaluation shows that 20 compounds passed the stringent lead-like criteria (250 ≤ MW≤350 & XLOGP ≤ 3.5 & Number of rotatable bonds≤7) [40] and in silico drug-likeness test, and showed high gastrointestinal absorption.
T333 42506-42683 Sentence denotes Moreover, predicted toxicity evaluation showed that the median lethal dose (LD50) of all these ingredients was above 1600 mg/kg, thus may suggesting safety and efficacy of QFPD.
T334 42684-43018 Sentence denotes Combined with molecular docking results, 4 specific ingredients (M3, S1, X2 and O2) and 5 common ingredients (MS1, MX16, SX1, WO1 and XO1) of QFPD might be promising leading compounds with good molecular docking score for 2019-nCov structure and non-structure proteins, revealing that QFPD treated COVID-19 by multi-component synergy.
T335 43019-43093 Sentence denotes However, these newly monomer components should provide a further research.
T336 43094-43208 Sentence denotes It has been reported that host cellular microRNAs (miRNAs) are involved in the regulation of virus infection [41].
T337 43209-43340 Sentence denotes A previous study discovered that significantly up-regulated MIR301 and down-regulated MIR183/130B were found in H1N1 patients [42].
T338 43341-43530 Sentence denotes Consistent with these results, we found that MIR183 and MIR130A/B/301 are related to four functional units of QFPD, indicating these microRNAs may exert anti−COVID-19 activity through QFPD.
T339 43531-43740 Sentence denotes In addition, CDKs have played a role in the efficient replication of various viruses, including human HIV-1, papillomaviruses, human cytomegalovirus (HCMV), herpes simplex virus (HSV) type 1 and HSV-2 [43,44].
T340 43741-43972 Sentence denotes In agreement with these results, we found that CDK7 was predicted to enriched in the five formulae, suggesting that QFPD may regulate replication of COVID-19 viruses via CDK7 mediated cell cycle and RNA polymerase II transcription.
T341 43973-44151 Sentence denotes Recently, a previous study showed that LXR known to regulate cholesterol homeostasis during inflammation were differentially regulated during H1N1 influenza virus infection [45].
T342 44152-44346 Sentence denotes Based on our results that LXR was associated with MSXG, SGMH, XCH, WLS targets, we speculated that QFPD can regulate metabolic and pro-inflammatory processes to counter COVID-19 virus infection.
T343 44347-44406 Sentence denotes In summary, QFPD is effective in the treatment of COVID-19.
T344 44407-44584 Sentence denotes However, some shortcomings in our study include lack of an in-depth study of predictive monomers and key targets and pathways, thus need further validation in vivo and in vitro.
T345 44585-44851 Sentence denotes And the TCMATCOV platform uses SARS disease network, which is different from the COVID-19 disease network, and COVID-19-related cytokines are related to severe COVID-19 disease, so the results of platform analysis more reflect the potential efficacy of severe stage.
T346 44852-45007 Sentence denotes Nevertheless, this study confirms that network pharmacology can help explore the mechanism of QFPD on the treatment of COVID-19 with time- and cost-saving.
T347 45008-45200 Sentence denotes Moreover, based on our new FUNP analysis, we reveal that QFPD treat COVID-19 by a holistic treatment and multi-component synergy, and are further demonstrated by formula perturbation analysis.
T348 45201-45352 Sentence denotes In addition, this study provides possible candidate monomers of QFPD and related miRNAs, kinases and TFs with potential therapeutic effect on COVID-19.
T349 45353-45470 Sentence denotes This will hopefully provide evidence and new insights for further researches on the treatment of COVID-19 using QFPD.
T350 45472-45492 Sentence denotes Author contributions
T351 45493-45929 Sentence denotes Conceiving the research, Jian Chen and Yong-bing Cao; Data curation, Jian Chen, Wen-jie Sun, and Zhi-qiang Liang; Funding acquisition, Jian Chen, Zhi-qiang Liang, Bing-yong Cao and Ye-min Cao; Investigation, Ling-San Hu, Jian-ru Wang, and Bing-yong Cao; Methodology, Yong-kui Wang, Jiang-wei Yang and Ye-min Cao; Resources, Ling-San Hu and Yong-kui Wang; Visualization, Jian-ru Wang, Jiang-wei Yang; Writing – original draft, Jian Chen.
T352 45931-45964 Sentence denotes Declaration of Competing Interest
T353 45965-46009 Sentence denotes The authors declare no conflict of interest.