PMC:7532482 / 11470-17207 JSONTXT

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T26","span":{"begin":1196,"end":1200},"obj":"Body_part"},{"id":"T27","span":{"begin":3888,"end":3906},"obj":"Body_part"},{"id":"T28","span":{"begin":4977,"end":4990},"obj":"Body_part"},{"id":"T29","span":{"begin":5066,"end":5079},"obj":"Body_part"},{"id":"T30","span":{"begin":5102,"end":5106},"obj":"Body_part"},{"id":"T31","span":{"begin":5154,"end":5158},"obj":"Body_part"},{"id":"T32","span":{"begin":5204,"end":5208},"obj":"Body_part"},{"id":"T33","span":{"begin":5460,"end":5464},"obj":"Body_part"}],"attributes":[{"id":"A26","pred":"fma_id","subj":"T26","obj":"http://purl.org/sig/ont/fma/fma74402"},{"id":"A27","pred":"fma_id","subj":"T27","obj":"http://purl.org/sig/ont/fma/fma82785"},{"id":"A28","pred":"fma_id","subj":"T28","obj":"http://purl.org/sig/ont/fma/fma76497"},{"id":"A29","pred":"fma_id","subj":"T29","obj":"http://purl.org/sig/ont/fma/fma76497"},{"id":"A30","pred":"fma_id","subj":"T30","obj":"http://purl.org/sig/ont/fma/fma7195"},{"id":"A31","pred":"fma_id","subj":"T31","obj":"http://purl.org/sig/ont/fma/fma74402"},{"id":"A32","pred":"fma_id","subj":"T32","obj":"http://purl.org/sig/ont/fma/fma74402"},{"id":"A33","pred":"fma_id","subj":"T33","obj":"http://purl.org/sig/ont/fma/fma74402"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-UBERON","denotations":[{"id":"T12","span":{"begin":5102,"end":5106},"obj":"Body_part"}],"attributes":[{"id":"A12","pred":"uberon_id","subj":"T12","obj":"http://purl.obolibrary.org/obo/UBERON_0002048"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T51","span":{"begin":226,"end":228},"obj":"Disease"},{"id":"T52","span":{"begin":871,"end":873},"obj":"Disease"},{"id":"T53","span":{"begin":3622,"end":3624},"obj":"Disease"},{"id":"T55","span":{"begin":3706,"end":3708},"obj":"Disease"},{"id":"T57","span":{"begin":3987,"end":3989},"obj":"Disease"},{"id":"T58","span":{"begin":4471,"end":4485},"obj":"Disease"},{"id":"T59","span":{"begin":4478,"end":4485},"obj":"Disease"},{"id":"T60","span":{"begin":4497,"end":4518},"obj":"Disease"},{"id":"T61","span":{"begin":4509,"end":4518},"obj":"Disease"},{"id":"T62","span":{"begin":4580,"end":4605},"obj":"Disease"},{"id":"T63","span":{"begin":4596,"end":4605},"obj":"Disease"},{"id":"T64","span":{"begin":4635,"end":4646},"obj":"Disease"},{"id":"T65","span":{"begin":4635,"end":4644},"obj":"Disease"},{"id":"T66","span":{"begin":4969,"end":4975},"obj":"Disease"},{"id":"T67","span":{"begin":4977,"end":4990},"obj":"Disease"},{"id":"T68","span":{"begin":5026,"end":5049},"obj":"Disease"},{"id":"T69","span":{"begin":5066,"end":5079},"obj":"Disease"},{"id":"T70","span":{"begin":5128,"end":5143},"obj":"Disease"},{"id":"T71","span":{"begin":5134,"end":5143},"obj":"Disease"}],"attributes":[{"id":"A51","pred":"mondo_id","subj":"T51","obj":"http://purl.obolibrary.org/obo/MONDO_0008380"},{"id":"A52","pred":"mondo_id","subj":"T52","obj":"http://purl.obolibrary.org/obo/MONDO_0008380"},{"id":"A53","pred":"mondo_id","subj":"T53","obj":"http://purl.obolibrary.org/obo/MONDO_0009691"},{"id":"A54","pred":"mondo_id","subj":"T53","obj":"http://purl.obolibrary.org/obo/MONDO_0020481"},{"id":"A55","pred":"mondo_id","subj":"T55","obj":"http://purl.obolibrary.org/obo/MONDO_0009691"},{"id":"A56","pred":"mondo_id","subj":"T55","obj":"http://purl.obolibrary.org/obo/MONDO_0020481"},{"id":"A57","pred":"mondo_id","subj":"T57","obj":"http://purl.obolibrary.org/obo/MONDO_0010873"},{"id":"A58","pred":"mondo_id","subj":"T58","obj":"http://purl.obolibrary.org/obo/MONDO_0005055"},{"id":"A59","pred":"mondo_id","subj":"T59","obj":"http://purl.obolibrary.org/obo/MONDO_0005089"},{"id":"A60","pred":"mondo_id","subj":"T60","obj":"http://purl.obolibrary.org/obo/MONDO_0005794"},{"id":"A61","pred":"mondo_id","subj":"T61","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A62","pred":"mondo_id","subj":"T62","obj":"http://purl.obolibrary.org/obo/MONDO_0005132"},{"id":"A63","pred":"mondo_id","subj":"T63","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"},{"id":"A64","pred":"mondo_id","subj":"T64","obj":"http://purl.obolibrary.org/obo/MONDO_0005344"},{"id":"A65","pred":"mondo_id","subj":"T65","obj":"http://purl.obolibrary.org/obo/MONDO_0002251"},{"id":"A66","pred":"mondo_id","subj":"T66","obj":"http://purl.obolibrary.org/obo/MONDO_0004979"},{"id":"A67","pred":"mondo_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/MONDO_0002465"},{"id":"A68","pred":"mondo_id","subj":"T68","obj":"http://purl.obolibrary.org/obo/MONDO_0024355"},{"id":"A69","pred":"mondo_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/MONDO_0002465"},{"id":"A70","pred":"mondo_id","subj":"T70","obj":"http://purl.obolibrary.org/obo/MONDO_0005108"},{"id":"A71","pred":"mondo_id","subj":"T71","obj":"http://purl.obolibrary.org/obo/MONDO_0005550"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T105","span":{"begin":13,"end":19},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T106","span":{"begin":93,"end":99},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T107","span":{"begin":190,"end":192},"obj":"http://purl.obolibrary.org/obo/CLO_0003797"},{"id":"T108","span":{"begin":190,"end":192},"obj":"http://purl.obolibrary.org/obo/PR_000008725"},{"id":"T109","span":{"begin":283,"end":289},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T110","span":{"begin":421,"end":426},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T111","span":{"begin":472,"end":477},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T112","span":{"begin":496,"end":502},"obj":"http://purl.obolibrary.org/obo/CLO_0001658"},{"id":"T113","span":{"begin":601,"end":606},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T114","span":{"begin":646,"end":649},"obj":"http://purl.obolibrary.org/obo/CLO_0050884"},{"id":"T115","span":{"begin":746,"end":751},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T116","span":{"begin":764,"end":769},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T117","span":{"begin":808,"end":813},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T118","span":{"begin":859,"end":861},"obj":"http://purl.obolibrary.org/obo/CLO_0003797"},{"id":"T119","span":{"begin":859,"end":861},"obj":"http://purl.obolibrary.org/obo/PR_000008725"},{"id":"T120","span":{"begin":901,"end":903},"obj":"http://purl.obolibrary.org/obo/CLO_0050509"},{"id":"T121","span":{"begin":926,"end":931},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T122","span":{"begin":1069,"end":1070},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T123","span":{"begin":1084,"end":1089},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T124","span":{"begin":1169,"end":1174},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T125","span":{"begin":1196,"end":1200},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T126","span":{"begin":1280,"end":1285},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T127","span":{"begin":1373,"end":1378},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T128","span":{"begin":1428,"end":1433},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T129","span":{"begin":1543,"end":1548},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T130","span":{"begin":1605,"end":1607},"obj":"http://purl.obolibrary.org/obo/CLO_0001236"},{"id":"T131","span":{"begin":1652,"end":1657},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T132","span":{"begin":1737,"end":1742},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T133","span":{"begin":1835,"end":1840},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T134","span":{"begin":2116,"end":2121},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T135","span":{"begin":2135,"end":2136},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T136","span":{"begin":2263,"end":2268},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T137","span":{"begin":2342,"end":2343},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T138","span":{"begin":2380,"end":2381},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T139","span":{"begin":2421,"end":2426},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T140","span":{"begin":2478,"end":2479},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T141","span":{"begin":2929,"end":2934},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T142","span":{"begin":2936,"end":2937},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T143","span":{"begin":2964,"end":2969},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T144","span":{"begin":3120,"end":3125},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T145","span":{"begin":3152,"end":3153},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T146","span":{"begin":3220,"end":3222},"obj":"http://purl.obolibrary.org/obo/CLO_0001387"},{"id":"T147","span":{"begin":3229,"end":3234},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T148","span":{"begin":3304,"end":3309},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T149","span":{"begin":3310,"end":3315},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T150","span":{"begin":3330,"end":3335},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T151","span":{"begin":3366,"end":3371},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T152","span":{"begin":3432,"end":3437},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T153","span":{"begin":3618,"end":3620},"obj":"http://purl.obolibrary.org/obo/GO_0005575"},{"id":"T154","span":{"begin":3696,"end":3702},"obj":"http://purl.obolibrary.org/obo/CLO_0002223"},{"id":"T155","span":{"begin":3696,"end":3698},"obj":"http://purl.obolibrary.org/obo/GO_0005575"},{"id":"T156","span":{"begin":3950,"end":3958},"obj":"http://purl.obolibrary.org/obo/UBERON_0000158"},{"id":"T157","span":{"begin":3965,"end":3973},"obj":"http://purl.obolibrary.org/obo/UBERON_0000158"},{"id":"T158","span":{"begin":4380,"end":4385},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T159","span":{"begin":4529,"end":4538},"obj":"http://purl.obolibrary.org/obo/SO_0000418"},{"id":"T160","span":{"begin":4574,"end":4579},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T161","span":{"begin":4613,"end":4622},"obj":"http://purl.obolibrary.org/obo/SO_0000418"},{"id":"T162","span":{"begin":4645,"end":4646},"obj":"http://purl.obolibrary.org/obo/CLO_0001021"},{"id":"T163","span":{"begin":4827,"end":4828},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T164","span":{"begin":4977,"end":4990},"obj":"http://purl.obolibrary.org/obo/UBERON_0002186"},{"id":"T165","span":{"begin":5020,"end":5025},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_10239"},{"id":"T166","span":{"begin":5060,"end":5065},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_10239"},{"id":"T167","span":{"begin":5066,"end":5079},"obj":"http://purl.obolibrary.org/obo/UBERON_0002186"},{"id":"T168","span":{"begin":5102,"end":5106},"obj":"http://purl.obolibrary.org/obo/UBERON_0002048"},{"id":"T169","span":{"begin":5102,"end":5106},"obj":"http://www.ebi.ac.uk/efo/EFO_0000934"},{"id":"T170","span":{"begin":5128,"end":5133},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_10239"},{"id":"T171","span":{"begin":5154,"end":5158},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T172","span":{"begin":5204,"end":5208},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T173","span":{"begin":5389,"end":5394},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T174","span":{"begin":5460,"end":5464},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T175","span":{"begin":5502,"end":5507},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T176","span":{"begin":5605,"end":5610},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T177","span":{"begin":5694,"end":5695},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T67","span":{"begin":171,"end":173},"obj":"Chemical"},{"id":"T68","span":{"begin":181,"end":183},"obj":"Chemical"},{"id":"T69","span":{"begin":190,"end":192},"obj":"Chemical"},{"id":"T70","span":{"begin":199,"end":201},"obj":"Chemical"},{"id":"T71","span":{"begin":208,"end":210},"obj":"Chemical"},{"id":"T72","span":{"begin":838,"end":840},"obj":"Chemical"},{"id":"T73","span":{"begin":849,"end":851},"obj":"Chemical"},{"id":"T74","span":{"begin":859,"end":861},"obj":"Chemical"},{"id":"T75","span":{"begin":894,"end":896},"obj":"Chemical"},{"id":"T76","span":{"begin":1227,"end":1230},"obj":"Chemical"},{"id":"T78","span":{"begin":1320,"end":1323},"obj":"Chemical"},{"id":"T80","span":{"begin":1624,"end":1627},"obj":"Chemical"},{"id":"T82","span":{"begin":1743,"end":1746},"obj":"Chemical"},{"id":"T84","span":{"begin":2320,"end":2327},"obj":"Chemical"},{"id":"T85","span":{"begin":2348,"end":2356},"obj":"Chemical"},{"id":"T86","span":{"begin":2358,"end":2365},"obj":"Chemical"},{"id":"T87","span":{"begin":2386,"end":2394},"obj":"Chemical"},{"id":"T88","span":{"begin":2456,"end":2463},"obj":"Chemical"},{"id":"T89","span":{"begin":2484,"end":2492},"obj":"Chemical"},{"id":"T90","span":{"begin":2623,"end":2626},"obj":"Chemical"},{"id":"T92","span":{"begin":2665,"end":2668},"obj":"Chemical"},{"id":"T94","span":{"begin":3614,"end":3616},"obj":"Chemical"},{"id":"T95","span":{"begin":3618,"end":3620},"obj":"Chemical"},{"id":"T96","span":{"begin":3622,"end":3624},"obj":"Chemical"},{"id":"T97","span":{"begin":3686,"end":3688},"obj":"Chemical"},{"id":"T98","span":{"begin":3696,"end":3698},"obj":"Chemical"},{"id":"T99","span":{"begin":3706,"end":3708},"obj":"Chemical"},{"id":"T100","span":{"begin":3888,"end":3906},"obj":"Chemical"},{"id":"T101","span":{"begin":3920,"end":3928},"obj":"Chemical"},{"id":"T102","span":{"begin":4520,"end":4523},"obj":"Chemical"},{"id":"T103","span":{"begin":4607,"end":4609},"obj":"Chemical"},{"id":"T105","span":{"begin":5713,"end":5717},"obj":"Chemical"}],"attributes":[{"id":"A67","pred":"chebi_id","subj":"T67","obj":"http://purl.obolibrary.org/obo/CHEBI_72723"},{"id":"A68","pred":"chebi_id","subj":"T68","obj":"http://purl.obolibrary.org/obo/CHEBI_74061"},{"id":"A69","pred":"chebi_id","subj":"T69","obj":"http://purl.obolibrary.org/obo/CHEBI_74055"},{"id":"A70","pred":"chebi_id","subj":"T70","obj":"http://purl.obolibrary.org/obo/CHEBI_73645"},{"id":"A71","pred":"chebi_id","subj":"T71","obj":"http://purl.obolibrary.org/obo/CHEBI_73700"},{"id":"A72","pred":"chebi_id","subj":"T72","obj":"http://purl.obolibrary.org/obo/CHEBI_73645"},{"id":"A73","pred":"chebi_id","subj":"T73","obj":"http://purl.obolibrary.org/obo/CHEBI_74061"},{"id":"A74","pred":"chebi_id","subj":"T74","obj":"http://purl.obolibrary.org/obo/CHEBI_74055"},{"id":"A75","pred":"chebi_id","subj":"T75","obj":"http://purl.obolibrary.org/obo/CHEBI_72723"},{"id":"A76","pred":"chebi_id","subj":"T76","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A77","pred":"chebi_id","subj":"T76","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A78","pred":"chebi_id","subj":"T78","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A79","pred":"chebi_id","subj":"T78","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A80","pred":"chebi_id","subj":"T80","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A81","pred":"chebi_id","subj":"T80","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A82","pred":"chebi_id","subj":"T82","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A83","pred":"chebi_id","subj":"T82","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A84","pred":"chebi_id","subj":"T84","obj":"http://purl.obolibrary.org/obo/CHEBI_10043"},{"id":"A85","pred":"chebi_id","subj":"T85","obj":"http://purl.obolibrary.org/obo/CHEBI_57284"},{"id":"A86","pred":"chebi_id","subj":"T86","obj":"http://purl.obolibrary.org/obo/CHEBI_10043"},{"id":"A87","pred":"chebi_id","subj":"T87","obj":"http://purl.obolibrary.org/obo/CHEBI_57284"},{"id":"A88","pred":"chebi_id","subj":"T88","obj":"http://purl.obolibrary.org/obo/CHEBI_10043"},{"id":"A89","pred":"chebi_id","subj":"T89","obj":"http://purl.obolibrary.org/obo/CHEBI_57284"},{"id":"A90","pred":"chebi_id","subj":"T90","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A91","pred":"chebi_id","subj":"T90","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A92","pred":"chebi_id","subj":"T92","obj":"http://purl.obolibrary.org/obo/CHEBI_53266"},{"id":"A93","pred":"chebi_id","subj":"T92","obj":"http://purl.obolibrary.org/obo/CHEBI_60614"},{"id":"A94","pred":"chebi_id","subj":"T94","obj":"http://purl.obolibrary.org/obo/CHEBI_29865"},{"id":"A95","pred":"chebi_id","subj":"T95","obj":"http://purl.obolibrary.org/obo/CHEBI_28940"},{"id":"A96","pred":"chebi_id","subj":"T96","obj":"http://purl.obolibrary.org/obo/CHEBI_74708"},{"id":"A97","pred":"chebi_id","subj":"T97","obj":"http://purl.obolibrary.org/obo/CHEBI_29865"},{"id":"A98","pred":"chebi_id","subj":"T98","obj":"http://purl.obolibrary.org/obo/CHEBI_28940"},{"id":"A99","pred":"chebi_id","subj":"T99","obj":"http://purl.obolibrary.org/obo/CHEBI_74708"},{"id":"A100","pred":"chebi_id","subj":"T100","obj":"http://purl.obolibrary.org/obo/CHEBI_16412"},{"id":"A101","pred":"chebi_id","subj":"T101","obj":"http://purl.obolibrary.org/obo/CHEBI_25367"},{"id":"A102","pred":"chebi_id","subj":"T102","obj":"http://purl.obolibrary.org/obo/CHEBI_84123"},{"id":"A103","pred":"chebi_id","subj":"T103","obj":"http://purl.obolibrary.org/obo/CHEBI_63895"},{"id":"A104","pred":"chebi_id","subj":"T103","obj":"http://purl.obolibrary.org/obo/CHEBI_74072"},{"id":"A105","pred":"chebi_id","subj":"T105","obj":"http://purl.obolibrary.org/obo/CHEBI_23888"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PubTator","denotations":[{"id":"166","span":{"begin":181,"end":183},"obj":"Gene"},{"id":"167","span":{"begin":199,"end":201},"obj":"Gene"},{"id":"170","span":{"begin":849,"end":851},"obj":"Gene"},{"id":"171","span":{"begin":838,"end":840},"obj":"Gene"},{"id":"173","span":{"begin":1867,"end":1872},"obj":"Gene"},{"id":"184","span":{"begin":2450,"end":2452},"obj":"Gene"},{"id":"185","span":{"begin":2320,"end":2327},"obj":"Chemical"},{"id":"186","span":{"begin":2329,"end":2343},"obj":"Chemical"},{"id":"187","span":{"begin":2348,"end":2356},"obj":"Chemical"},{"id":"188","span":{"begin":2358,"end":2365},"obj":"Chemical"},{"id":"189","span":{"begin":2367,"end":2381},"obj":"Chemical"},{"id":"190","span":{"begin":2386,"end":2394},"obj":"Chemical"},{"id":"191","span":{"begin":2456,"end":2463},"obj":"Chemical"},{"id":"192","span":{"begin":2465,"end":2479},"obj":"Chemical"},{"id":"193","span":{"begin":2484,"end":2492},"obj":"Chemical"},{"id":"195","span":{"begin":3304,"end":3309},"obj":"Species"},{"id":"198","span":{"begin":3888,"end":3906},"obj":"Chemical"},{"id":"199","span":{"begin":4009,"end":4014},"obj":"Disease"},{"id":"207","span":{"begin":4520,"end":4523},"obj":"Gene"},{"id":"208","span":{"begin":4524,"end":4528},"obj":"Gene"},{"id":"209","span":{"begin":4607,"end":4612},"obj":"Gene"},{"id":"210","span":{"begin":4471,"end":4518},"obj":"Disease"},{"id":"211","span":{"begin":4550,"end":4572},"obj":"Disease"},{"id":"212","span":{"begin":4580,"end":4605},"obj":"Disease"},{"id":"213","span":{"begin":4635,"end":4646},"obj":"Disease"},{"id":"217","span":{"begin":4977,"end":5036},"obj":"Disease"},{"id":"218","span":{"begin":5038,"end":5079},"obj":"Disease"},{"id":"219","span":{"begin":5128,"end":5151},"obj":"Disease"},{"id":"226","span":{"begin":5435,"end":5439},"obj":"Gene"},{"id":"227","span":{"begin":5539,"end":5544},"obj":"Gene"},{"id":"228","span":{"begin":5546,"end":5552},"obj":"Gene"},{"id":"229","span":{"begin":5554,"end":5559},"obj":"Gene"},{"id":"230","span":{"begin":5561,"end":5566},"obj":"Gene"},{"id":"231","span":{"begin":5571,"end":5574},"obj":"Gene"}],"attributes":[{"id":"A166","pred":"tao:has_database_id","subj":"166","obj":"Gene:3643"},{"id":"A167","pred":"tao:has_database_id","subj":"167","obj":"Gene:140738"},{"id":"A170","pred":"tao:has_database_id","subj":"170","obj":"Gene:3643"},{"id":"A171","pred":"tao:has_database_id","subj":"171","obj":"Gene:140738"},{"id":"A184","pred":"tao:has_database_id","subj":"184","obj":"Gene:1009"},{"id":"A185","pred":"tao:has_database_id","subj":"185","obj":"MESH:C085514"},{"id":"A186","pred":"tao:has_database_id","subj":"186","obj":"MESH:C070840"},{"id":"A187","pred":"tao:has_database_id","subj":"187","obj":"MESH:C023717"},{"id":"A188","pred":"tao:has_database_id","subj":"188","obj":"MESH:C085514"},{"id":"A189","pred":"tao:has_database_id","subj":"189","obj":"MESH:C070840"},{"id":"A190","pred":"tao:has_database_id","subj":"190","obj":"MESH:C023717"},{"id":"A191","pred":"tao:has_database_id","subj":"191","obj":"MESH:C085514"},{"id":"A192","pred":"tao:has_database_id","subj":"192","obj":"MESH:C070840"},{"id":"A193","pred":"tao:has_database_id","subj":"193","obj":"MESH:C023717"},{"id":"A195","pred":"tao:has_database_id","subj":"195","obj":"Tax:9606"},{"id":"A198","pred":"tao:has_database_id","subj":"198","obj":"MESH:D008070"},{"id":"A199","pred":"tao:has_database_id","subj":"199","obj":"MESH:D003643"},{"id":"A207","pred":"tao:has_database_id","subj":"207","obj":"Gene:5973"},{"id":"A208","pred":"tao:has_database_id","subj":"208","obj":"Gene:177"},{"id":"A209","pred":"tao:has_database_id","subj":"209","obj":"Gene:3605"},{"id":"A210","pred":"tao:has_database_id","subj":"210","obj":"MESH:D012514"},{"id":"A211","pred":"tao:has_database_id","subj":"211","obj":"MESH:D048909"},{"id":"A212","pred":"tao:has_database_id","subj":"212","obj":"MESH:D003586"},{"id":"A213","pred":"tao:has_database_id","subj":"213","obj":"MESH:D006509"},{"id":"A219","pred":"tao:has_database_id","subj":"219","obj":"MESH:D001102"},{"id":"A226","pred":"tao:has_database_id","subj":"226","obj":"Gene:5970"},{"id":"A227","pred":"tao:has_database_id","subj":"227","obj":"Gene:5594"},{"id":"A228","pred":"tao:has_database_id","subj":"228","obj":"Gene:1432"},{"id":"A229","pred":"tao:has_database_id","subj":"229","obj":"Gene:836"},{"id":"A230","pred":"tao:has_database_id","subj":"230","obj":"Gene:841"},{"id":"A231","pred":"tao:has_database_id","subj":"231","obj":"Gene:3569"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T19","span":{"begin":4471,"end":4485},"obj":"Phenotype"},{"id":"T20","span":{"begin":4635,"end":4644},"obj":"Phenotype"},{"id":"T21","span":{"begin":4969,"end":4975},"obj":"Phenotype"},{"id":"T22","span":{"begin":4977,"end":4990},"obj":"Phenotype"},{"id":"T23","span":{"begin":5066,"end":5079},"obj":"Phenotype"}],"attributes":[{"id":"A19","pred":"hp_id","subj":"T19","obj":"http://purl.obolibrary.org/obo/HP_0100726"},{"id":"A20","pred":"hp_id","subj":"T20","obj":"http://purl.obolibrary.org/obo/HP_0012115"},{"id":"A21","pred":"hp_id","subj":"T21","obj":"http://purl.obolibrary.org/obo/HP_0002099"},{"id":"A22","pred":"hp_id","subj":"T22","obj":"http://purl.obolibrary.org/obo/HP_0011950"},{"id":"A23","pred":"hp_id","subj":"T23","obj":"http://purl.obolibrary.org/obo/HP_0011950"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T8","span":{"begin":552,"end":559},"obj":"http://purl.obolibrary.org/obo/GO_0015292"},{"id":"T9","span":{"begin":3876,"end":3906},"obj":"http://purl.obolibrary.org/obo/GO_0032496"},{"id":"T10","span":{"begin":3908,"end":3948},"obj":"http://purl.obolibrary.org/obo/GO_0002237"},{"id":"T11","span":{"begin":4529,"end":4546},"obj":"http://purl.obolibrary.org/obo/GO_0007165"},{"id":"T12","span":{"begin":4529,"end":4538},"obj":"http://purl.obolibrary.org/obo/GO_0023052"},{"id":"T13","span":{"begin":4613,"end":4630},"obj":"http://purl.obolibrary.org/obo/GO_0007165"},{"id":"T14","span":{"begin":4613,"end":4622},"obj":"http://purl.obolibrary.org/obo/GO_0023052"}],"text":"Results\n\nThe active ingredients of each herb contained in SFJDC\nOne hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}

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Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation (Table 2).\n\nPutative target genes of each herb in SFJDC and NCP related target genes\nThe 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation (Table 3). And 251 NCP related target genes were identified from Gene Cards database (Table 4).\nPPI network of SFJDC putative and NCP related target genes\nIn this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges (Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.\n\nSFJDC ingredient-target network analysis\nThe ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.\n\nPPI network analysis of SFJDC against NCP\nPPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges (Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges (Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.\n\nIdentification of candidate genes (CGs) and Enrichment analysis of CGs\nTwenty-three candidate genes (CGs) were identified by using the VennDiagram package (Figure 5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P\u003c0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.\nThe pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P\u003c0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data (Table 6), the top five pathways such as Kaposi sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, IL-17 signaling pathway and Hepatitis B, might be the core pharmacological mechanism of SFJDC for NCP.\nIn this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained (Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.\n\nGene-pathway network analysis\nThe construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP."}