key: cord-1009274-kagggou1 authors: liu, Chang; Yin, Zhigang; Feng, Tingting; Zhang, Min; Zhou, Zhi; Zhou, Ying title: An integrated network pharmacology and RNA-Seq approach for exploring the preventive effect of Lonicerae japonicae flos on LPS-induced acute lung injury date: 2020-09-09 journal: J Ethnopharmacol DOI: 10.1016/j.jep.2020.113364 sha: 1eab38d2cf808ff238e32172416e47e0f7262d0e doc_id: 1009274 cord_uid: kagggou1 ETHNOPHARMACOLOGICAL RELEVANCE: Lonicerae japonicae flos (LJF, the dried flower bud or newly bloomed flower of Lonicera japonica Thunb.), a typical herbal medicine, targets the lung, heart and stomach meridian with the function of clearing heat and detoxication. It ameliorated inflammatory responses and protected against acute lung inflammation in animal models. Acute lung injury (ALI) is a kind of inflammatory disease in which alveolar cells are damaged. However, a network pharmacology study to thoroughly investigate the mechanisms preventing ALI has not been performed. AIM OF THE STUDY: In this study, we examined the main active ingredients in LJF and the protective effects of LJF on LPS-induced ALI in rats. MATERIALS AND METHODS: First, the main active ingredients of LJF were screened in the TCMSP database, and the ALI-associated targets were collected from the GeneCards database. Then, we used compound-target and target-pathway networks to uncover the preventive mechanisms of LJF. Furthermore, we assessed the preventive effects of LJF in an LPS-induced rat model with the RNA-Seq technique to validate the possible molecular mechanisms of the effects of LJF in the treatment of ALI. RESULTS: The network pharmacology results identified 28 main active compounds in LJF, and eight chemical components highly related to the potential targets, which were potential active compounds in LJF. In all, 94 potential targets were recognized, including IL6, TNF, PTGS2, APP, F2, and GRM5. The pathways revealed that the possible targets of LJF involved in the regulation of the IL-17 signalling pathway. Then, in vivo experiments indicated that LJF decreased the levels of proinflammatory cytokines (TNF-, IL-1, and IL-6) in serum and bronchoalveolar lavage fluid, decreased the levels of oxidative stress factors (MDA and MPO) and increased the activities of SOD and GSH-Px in lung tissue. The RNA-Seq results revealed that 7811, 775 and 3654 differentially expressed genes (DEGs) in Ctrl (control group), ALI-LJF (Lonicerae japonicae flos group) and ALI-DXSM (dexamethasone group), respectively. KEGG pathway analysis showed that the DEGs associated with immune response and inflammation signalling pathways and the IL-17 signalling pathway were significantly enriched in LJF. Compared with those in ALI, the expression of CXCL2, CXCL1, CXCL6, NFKBIA, IFNG, IL6, IL17A, IL17F, IL17C, MMP9 and TNFAIP3, which are involved in the IL-17 signalling pathway, were significantly decreased in the LJF group according to the qRT-PCR analyses. CONCLUSIONS: In view of the network pharmacology and RNA-Seq results, the study identified the main active ingredient and potential targets of LJF involved in protecting against ALI, which suggests directions for further research on LJF. F2, and GRM5. The pathways revealed that the possible targets of LJF involved in the regulation 29 of the IL-17 signalling pathway. Then, in vivo experiments indicated that LJF decreased the levels 30 of proinflammatory cytokines (TNF-, IL-1 , and IL-6) in serum and bronchoalveolar lavage 31 fluid, decreased the levels of oxidative stress factors (MDA and MPO) and increased the activities 32 obtain the aqueous extract, which was kept at -20 ℃ until use. Prior to HPLC analysis, the 116 solution was filtered through a 0.45 μm membrane. The chromatographic fingerprinting of LJF 117 showed 14 main peaks, which were identified as 2 (neochlorogenic acid), 5 (chlorogenic acid), 6 118 (chlorogenic acid), 10 (secoxyloganin), 11 (isochlorogenic acid B), 12 (isochlorogenic acid A), 119 and 14 (isochlorogenic acid). The method could be used to identify the characteristics of LJF (Fig. 120 S1 ). 121 For further analyses, the bronchoalveolar lavage fluid (BALF), lung tissue and blood were 136 collected and stored at −80 °C. 137 After treatment, the levels of IL-1β, IL-6, and TNF-α in the serum and BALF were 139 determined with an enzyme-linked immunosorbent assay (ELISA) kit, and an ELISA kit was used 140 to determine the levels of GSH-Px, MPO, MDA, and SOD in the lung tissue. All data are 141 presented as the mean ± SD. GraphPad Prism 6 software was employed for one-way ANOVA, and 142 a P-value ≤ 0.05 was confirmed to be statistically significant. 143 The rat lung tissues were fixed in 4% formaldehyde, embedded in paraffin and cut into 5 μm 145 sections. Then, the sections underwent haematoxylin-eosin (H&E) staining. The changes in 146 pulmonary histopathology were visualized under a microscope, and the pathological scores were 147 obtained. The degree of pulmonary injury was evaluated according to a previous report (Liu et al., 148 2018) . 149 Total RNA was extracted with TRIzol reagent (TIANGEN, China) and detected on a 1% 151 agarose gel. The purity, concentration, and integrity of the total RNA samples were assessed prior 152 to further analysis. After cluster generation, the library preparations were sequenced on the 153 Illumina HiSeqTM 4000 platform by Biomarker Technologies (Beijing, China), and the raw reads 154 were generated. Then, the raw reads were filtered by removing adapter and poly-N sequences and 155 inferior quality reads from the raw reads. The clean reads were mapped to the Rattus norvegicus 156 (Rnor_6.0) reference genome sequence by the HISAT2 tools. The levels of quantitative gene 157 expression were estimated by determining the fragments per kilobase of transcript per million 158 fragments mapped. Gene expression analysis of the different groups was performed by DESeq2. 159 Genes with a P-value < 0.05 were defined as differentially expressed genes (DEGs). Then, we 160 used the KOBAS3.0 platform to perform the enrichment analysis of the DEGs (Xie et al., 2011) . 161 The volcano plot and heatmap were generated with the OmicShare online tools 162 The mRNA expression of 170 BCL2A1, CXCL1, CXCL2, CXCL6, DNAJC5, FADD, KIT, IFNG, IL1R1, IL6, IL17A, IL17F, 171 IL17C, ITGA5, MMP9, NFKBIA, NPLOC4, PPP1R15A, THBS1, TNFAIP3, TRADD, and 172 GAPDH were detected by qRT-PCR in the lung tissues. The genes primer sequences (Table S9 ) 173 J o u r n a l P r e -p r o o f for qRT-PCR were designed the primer3 platform (http://frodo.wi.mit.edu/primer3/). All 174 qRT-PCR was repeated three times, the expression levels of candidate genes were determined 175 using the 2 -ΔΔCT method. Expression levels were normalized against the reference gene GAPDH. 176 Data are represented as mean values ± SD, and GraphPad Prism 6 software was employed for 177 one-way ANOVA, *P<0.05, ** P<0.01. 178 In total, 23 ingredients in LJF fulfilled the two criteria simultaneously (DL ≥ 0.18 and OB ≥ 181 30%). However, five ingredients (scolymoside, hederagenol, heriguard, hyperin, and 182 luteolin-7-o-glucoside) did not meet the criteria of OB ≥ 30%, but they were also presented as 183 candidates. Hence, 28 main ingredients were obtained as potentially active constituents in LJF, 184 including flavonoids and organic acids (Table S1 ). These 28 active components were used to 185 identify the targets in SwissTargetPrediction and SEA. After removing duplicates, we finally 186 acquired 212 targets (Table S2 ). The ALI-related genes were collected from the GeneCards 187 database, and 1047 ALI-related genes were identified (Table S3 ). The shared targets of the 28 188 active compounds and ALI-related genes were identified by generating Venn diagrams. Ultimately, 189 94 genes were identified as both the targets of active ingredients and ALI-related genes. 190 The 94 identified genes were used to construct the compound-target network with Cytoscape 192 3.7.1 software. Among the active compounds in LJF, eight active compounds demonstrated a 193 higher number of connections and were connected with more than 20 targets, including 194 ethyl-linolenate, hederagenol, chrysoeriol, kaempferol, luteolin, mandenol, quercetin and 195 ZINC03978781. The network analysis showed that one compound in the herbs can be linked with 196 more than one target. In the compound-target network, ALOX5 (arachidonate 5-lipoxygenase) 197 was simultaneously targeted by 11 active ingredients, AKR1B1 (aldo-keto reductase family 1 198 member B1) was targeted by 10 active ingredients, and PTGS2 (prostaglandin G/H synthase 2) 199 was targeted by 10 active ingredients (Fig.1.) . In the PPI network, eleven targets were linked 200 PTGS2 might be identified as hub genes. 204 The 94 potential target proteins were subjected to enrichment analysis by Kobas3.0. As a 206 result, 84 significant pathways related to LJF were identified by Kobas3.0 (Table S4) . Then, the 207 target-pathway network was constructed (Fig.S2 ), containing 86 targets and 84 corresponding 208 pathways. It was obvious that the targets for LJF were mainly interlinked in three pathways, 209 including metabolic pathways (degree = 25), neuroactive ligand-receptor interaction pathways 210 (degree = 23), and pathways involved in cancer (degree = 19). Moreover, we identified the 211 calcium signalling pathway (degree = 12) and NOD-like receptor signalling pathway (degree = 212 10). Fortunately, we also observed that these targets participated in pathways linked to immune 213 and inflammatory signalling pathways, including human cytomegalovirus infection-associated 214 pathways, the IL-17 signalling pathway, the PPAR signalling pathway, the PI3K-Akt signalling 215 pathway, human T-cell leukaemia virus 1 infection-associated pathways, pathways involved in 216 inflammatory mediator regulation of TRP channels, the Jak-STAT signalling pathway, natural 217 killer cell-mediated cytotoxicity-associated pathways, T cell receptor signalling pathway, 218 NF-kappa B signalling pathway, TNF signalling pathway and the Toll-like receptor signalling 219 pathway. 220 To detect the anti-inflammatory effects of LJF, we examined the levels of IL-6, TNF-α, and 222 IL-1β in serum and BALF after treatment with LPS, LJF and DXSM. The results showed that 223 exposure of serum and BALF to LPS increased the production of these inflammatory cytokines 224 (P<0.05). However, production of these inflammatory cytokines (IL-6, TNF-α, and IL-1β) were 225 significantly inhibited by LJF and DXSM (P<0.05) ( Fig.3A and 3B) . Then, the level of oxidative 226 stress was measured, and LPS-induced ALI could increase the levels of MDA and MPO and 227 decrease the activities of SOD and GSH-Px in the lung tissue (P<0.05) (Fig.3C) . Interestingly, the 228 levels of SOD and GSH-Px were significantly enhanced by LJF and DXSM (P<0.05). 229 H&E staining was used to detect the pathological changes in the lungs. After treatment with 231 LPS, the rat lung tissue showed increases in inflammatory cell infiltrates and alveolar histological 232 structure damage compared with the lung tissue in the Ctrl group. In our study, the ALI group 233 showed severe alveolar oedema fluid accumulation, alveolar capillary congestion and bronchial 234 epithelial detachment (Fig.4B) . LJF was superior to DXSM in alleviating LPS-induced ALI ( (Table S5) . To determine the differentially 246 expressed genes (DEGs), a P-value < 0.05 was used as the cut-off value for gene expression in the 247 Ctrl, ALI, ALI-LJF, and ALI-DXMS groups, which was detected by pairwise comparisons 248 between the ALI group and the Ctrl, ALI-LJF, and ALI-DXMS groups. As a result, 7,811 DEGs 249 in rat lung were identified after LPS stimulation, whereas 775 and 3,654 DEGs were identified in 250 rat lung tissue treated with LJF and DXSM, respectively. Overall, 1,688 upregulated and 1,966 251 downregulated DEGs were identified in the ALI vs. ALI-DXSM groups, and 340 upregulated and 252 435 downregulated DEGs were identified in the ALI vs. ALI-LJF groups ( Fig.5A-C) . In brief, 253 after removing the duplicate genes, 509 DEG genes were associated with the ALI group that were 254 also affected in the Ctrl, ALI and ALI-LJF groups; 2,546 genes were associated with the Ctrl, ALI 255 and ALI-DXSM groups; and only 298 key DEG genes were associated with the Ctrl, ALI, 256 ALI-LJF and ALI-DXSM groups (Fig.5D) . 257 To characterize these differentially expressed genes, trend analysis was applied to determine 258 the expression patterns of the 509 DEGs in the Ctrl, ALI and ALI-LJF groups (Table S6 ). In the 259 J o u r n a l P r e -p r o o f ALI vs. Ctrl groups, the expression of 255 genes displayed an initial increase, but there was a 260 decrease in the ALI vs ALI-LJF groups; however, the expression of 217 genes exhibited a 261 reduction in the ALI vs. ALI-LJF groups, but there was a subsequent increase in the ALI vs. 262 ALI-LJF groups (Fig.6) . Specifically, eight genes (DYRK1A, CA4, IL6, PTAFR, ARG1, MGLL, 263 LTB4R, and TYMP) among the DEGs of the ALI-LJF group were predicted as targets of active 264 ingredients of LJF. 265 To verify the reliability of the gene expression data obtained by RNA-Seq, eight genes 267 (IL1R1, DNAJC5, THBS1, NPLOC4, BCL2A1, PPP1R15A, KIT, and ITGA5) were randomly 268 selected for qRT-PCR detection. The qRT-PCR results showed that the tendency of gene 269 expression was consistent with the RNA-Seq results. For each gene, the qRT-PCR expression 270 results showed a similar tendency to the RNA-Seq results (Fig.S3) . The results showed that the 271 RNA-Seq results were credible in this study. 272 To thoroughly investigate the potential pathways involved in the immune and inflammatory 274 responses, the Kobas3.0 platform was employed for KEGG pathway analysis of these DEGs. The 275 KEGG analysis showed that 40 KEGG pathways were significantly enriched for these DEGs 276 (Table S7; signalling pathway (Fig.7) . Within the four classic immune pathways (rno04657, rno04668, 283 rno04064, and rno04630), we identified 24 candidate genes associated with ALI: CXCL2, CXCL1, 284 CXCL6, IL10, LIF, IL12RB2, IL22, NFKBIA, IFNG, IL12A, IL6, BIRC2, IL17A, IL17C, IL17F, 285 CXCL12, IL1R1, TRADD, MMP9, CCND1, IL19 , FADD, BCL2A1, and TNFAIP3. 286 In this study, IL-17 signalling pathway involved in thirteen DEGs, including CXCL2, 288 CXCL1, CXCL6, NFKBIA, IFNG, IL6, IL17A, IL17F, IL17C, MMP9, TNFAIP3, FADD and 289 TRADD. LJF significantly inhibited CXCL2, CXCL1, CXCL6, NFKBIA, IFNG, IL6, IL17A, 290 IL17F, IL17C, MMP9, and TNFAIP3 mRNA expression in lung tissue homogenates according to 291 RNA-Seq, which indicates that the IL-17 signalling pathway is critical for treatment of 292 LPS-induced ALI with LJF (Fig.S4) . Interestingly, the involved IL-17 family members, including 293 IL-17A, IL-17C and IL-17F, played a significant role in the acute inflammatory responses 294 (Fig.S4) . Consistent with the RNA-Seq data, the expression of CXCL2, CXCL1, CXCL6, 295 NFKBIA, IFNG, IL6, IL17A, IL17F, IL17C, MMP9 and TNFAIP3 in lung tissue was 296 significantly decreased compared with that in the ALI and LJF groups according to the qRT-PCR 297 analyses (P<0.05) (Fig.8) . The expression of TRADD and FADD was increased compared with 298 that in the ALI and LJF groups by the qRT-PCR analyses (Fig.8) , and associated with apoptosis in 299 the IL-17 signalling pathway (Fig.S4) . 300 We employed network pharmacology to determine the potential active ingredients and targets 302 of LJF. Then, we performed compound-target and target-pathway network analyses to explore the 303 mechanisms of LJF. Furthermore, an LPS-induced rat model was constructed to evaluate the 304 effect of LJF in the treatment of ALI. According to the degree of the nodes in the compound-target 305 network, we identified eight compounds as potential active ingredients that might participate in 306 the regulatory processes of LJF in ALI. Obviously, the active ingredients chrysoeriol (Wei et qRT-PCR analysis showed that the trends for eight genes (IL1R1, DNAJC5, THBS1, NPLOC4, 340 BCL2A1, PPP1R15A, KIT, and ITGA5) were consistent with the RNA-Seq results. These 341 findings indicated that the RNA-Seq results were credible. By comparative analysis, we found that 342 there were more differentially expressed genes in the ALI, ALI-LJF and ALI-DXSM groups. In all, 343 192 DEGs were unique to the ALI-LJF group, and 298 DEGs were shared among the three groups. 344 Through KEGG enrichment analysis, we found that the IL-17 signalling pathway was 345 significantly enriched in the ALI-LJF group. 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