Active ingredients and target screening
A total of 206 compounds in XCD were selected from TCMIP and TCMSP database. Screening on the two criteria that OB thresholds ≥ 0.3 and DL ≥ 0.18, 46 candidate compounds in XCD were obtained: 16 in R. officinale Baill, 19 Semen Armeniacae Amarum and 11 in T. kirilowii Maxim (Table S2). Excluding three compounds which had no reported targets in the two databases or literatures, 43 compounds mainly belongs to flavonoids and organic acids were considered as potential active ingredients (Table 1). Screening in the TCMIP and TCMSP database, 281 compound-related targets were obtained (Table S3). Meanwhile, selecting from the three databases, GenCards, OMIM, and DisGeNET, 6783 disease-related targets were obtained (Table S4). The overlapping targets between the above two data sets (281 targets) were identified as candidate targets for XCD treatments on ALI.
Candidate compound-candidate target network construction and target enrichment analysis
The candidate compound-candidate target network was constructed by 43 candidate compounds and 281 candidate targets (Fig. 1, Table S5). Quercetin, amygdalin, stigmasterol, L-SPD, and emodin were the five most relevant candidate compounds, while prostaglandin-endoperoxide synthase 2 (PTGS2), nuclear receptor coactivator 2 (NCOA2), prostaglandin-endoperoxide synthase 1 (PTGS1), progesterone receptor (PGR), androgen receptor (AR), calmodulin (CAM), factor X (F10), heat shock protein 90 (HSP90), nuclear receptor subfamily3, group C, member2 (NR3C2), and nitric oxide synthase 2 (NOS2) were the ten most relevant candidate targets.
Then, the functional enrichment analysis of 281 candidate targets was conducted using the DAVID program. The KEGG pathway analysis (Fig. 2A) showed that the target genes were mainly associated with pathways related with immunity and inflammation, such as cancer (prostate, bladder, colorectal, small cell lung, and pancreatic) hepatitis B and apoptosis, and a variety of signaling-related pathways, such as HIF-1α, tumor necrosis factor (TNF), VEGF, mTOR, Forkhead box O (FOXO), PI3K-AKT, nuclear factor (NF)-κB, and nucleotide-binding oligomerization domain (NOD)-like receptor. The GO analyses obtained enriched results of those candidate target genes (Fig. 2B), which showed that LPS-mediated signaling pathway, cellular response to hypoxia，positive regulation of angiogenesis, extrinsic apoptotic signaling pathway in the absence of a ligand and cell-cell signaling, activation of cysteine-type endopeptidase activity involved in apoptotic processes were closely related to the activities of XCD on ALI.
DEGs identified by mRNA-seq and their pathway enrichment analysis
The mRNA profiles of lung tissues of rats in both the model and XCD high-dose groups (n = 3) were determined using mRNA-seq. In all, 22,062 genes were identified, which were expressed in at least one sample (FPKM cut-off value 0.01). The number of genes expressed in the lungs of rats with ALI was 16,436 in the Normal group, 16,257 in the Model group, 16,423 in the XCD group. To determine the differentially expressed genes (DEGs), a P value < 0.05 which was detected by pairwise comparisons between the Model group and the Normal, XCD and Model groups was used as the screening criteria for gene expression in the Normal, Model, and XCD groups. Overall, 1085 upregulated and 1768 downregulated DEGs were identified in the Model vs Normal groups (Fig. 3A), and 485 upregulated and 448 downregulated DEGs were identified in the XCD vs. Model groups (Fig. 3B). Based on the criteria of fold-change > 1 and P < 0.05, a total of 753 genes including 361 upregulated and 392 downregulated genes were identified between the model group and XCD treatment group (Table S6). The heat map function of the R package used for analyzing the 10% DEGs chosen randomly by using the FPKM value, which showed a significant difference in gene expression between the model group and XCD treatment group (Fig. 4A).
The interactions between the DEGs were analyzed using functional enrichment with the DAVID program. KEGG pathway and GO enrichment analysis showed that the DEGs were mainly associated with Pathways in cancer, Proteoglycans in cancer, Focal adhesion, MicroRNAs in cancer, Prostate cancer, Rheumatoid arthritis, Adrenergic signaling in cardiomyocytes, Tuberculosis, Hepatitis B, Toxoplasmosis, Malaria, Thyroid cancer, and a variety of signal-related pathways, such as PI3K-AKT, TNF, HIF-1, PPAR, NOD-like receptor, VEGF signaling pathways (Fig. 4B). Interestingly, some KEGG pathways of DEGs were overlapped with candidate target genes, which indicated some key target genes might exist, and the GO results of the functional analysis were also generally consistent with XCD network pharmacology (Fig. 4C). Taken together, the DEGs were closely related to the pulmonary inflammation storm and pulmonary edema of LPS-induced ALI, such as positive regulation of angiogenesis, positive regulation of apoptotic process, positive regulation of protein phosphorylation, negative regulation of cell proliferation, response to LPS, inflammatory responses and response to hypoxia.
PPI network construction and identification of kernel targets
Overlapping the candidate targets from network pharmacology analyses and the DEGs from RNA-seq, 57 genes were found and considered as key targets (Fig. 5A, Table S7, Table S8). Then, the shared 57 targets were used to construct the PPI network (Fig. 5B), which contained 57 nodes (of which there are no isolated targets) and 333 edges with an average nodal degree value of 11.6. There were 20 targets with degree values greater than the average value, which might be the key targets of XCD on ALI treatment (Fig. 5C). The top six genes with the highest nodal degree values, namely PIK3CA, MTOR, AKT1, PTEN, HIF1A, and VEGFA, were selected as kernel targets (Table 2). To further screen the corresponding pathways of the kernel targets, the kernel target-pathway network was constructed and KEGG analyses were conducted, indicating that the PI3K-AKT, mTOR, HIF-1α, and VEGF signaling pathways were closely related to these kernel genes (Fig. 5D, Table S9).
XCD treatment alleviated the pathological changes and inflammatory cytokines levels induced by LPS
XCD significantly alleviated lung tissue injury as well as inflammatory cell infiltration induced by LPS (Fig. 6A). In the model group, lung tissue showed significant interstitial pneumonia and edema around the small interstitial vessels, whereas in both XCD treatment pathological changes of lung tissue such as alveolar dilatation, emphysema, and interstitial pneumonia were found obviously decrease as well as those in DEX group. Also, XCD significantly decreased the levels of proinflammatory cytokines such as TNF-α, IL-6 and IL-1β in BALF (P < 0.05, respectively) (Fig. 6B).
XCD treated ALI by inhibiting the PI3K/AKT/mTOR signaling pathways
The mRNA and protein expressions of the six kernel targets, such as PI3K, AKT, PTEN, mTOR, VEGF, and HIF1A, in lung tissues were investigated. By qT-PCR analyses, the mRNA expressions of PTEN and mTOR were decreased (P < 0.05, respectively), and the mRNA expression of PI3K and AKT were increased (P < 0.05, respectively) with LPS stimulation. In contrast, XCD treatments with either low-dose or high-dose could significantly recover the mRNA expressions of the four targets (P < 0.05, respectively) (Fig. 6C). WB analyses showed similar changes in the protein expressions of kernel targets proteins in those groups (Fig. 6D). Meanwhile, the protein expressions of phosphorylated-mTOR (p-mTOR), phosphorylated-PI3K (p-PI3K), HIF-1α, and VEGF in lung tissues were significantly increased with LPS stimulation (P < 0.05, respectively). In contrast, the expressions of those proteins with two doses of XCD treatments showed significantly decrements, respectively (P < 0.05, respectively) (Fig. 6D). The observations by IHC analyses showed similar trends as those in WB analyses. The expressions of PI3K, AKT, HIF-1α, and VEGF in lung tissues were significantly increased (P < 0.01, respectively), while the expressions of PTEN and mTOR were significantly decreased (P < 0.01, respectively) with LPS stimulation. In contrast, the expressions of PI3K, AKT, PTEN, mTOR, HIF-1α, and VEGF, could be recovered significantly with both two doses of XCD treatments (P < 0.05 or P < 0.01, respectively), as well as those of DEX treatment except for mTOR (Fig. 7).