Multi-Target Mechanism of Compound Qingdai Capsule for Treatment of Psoriasis: Multi-Omics Analysis and Experimental Verification

Abstract

Background: Psoriasis is a chronic skin disease affected by genetic and autoimmunity. The traditional Chinese medicine, Compound Qingdai Capsule (CQC), has shown potential benefits in treating psoriasis in clinical settings. Despite its efficacy, the molecular mechanisms underpinning its therapeutic action remain unclear.

Purpose: This study aimed to unravel the molecular mechanism of Compound Qingdai Capsule for psoriasis based on the psoriasis pathogenic pathway network, integrating multi-omics analysis, systems pharmacology, machine learning modeling, and animal experimentation.

Methods: Psoriasis pathogenic pathway network was constructed through employing bioinformatics analysis and psoriasis-related multi-omics data mining. The ingredients of CQC were detected by UPLC-MS/MS, and target prediction was performed by systems pharmacology. Machine learning, including Lasso regression, Random Forest, and Support Vector Machine (SVM), were utilized to screen core targets of psoriasis. Molecular docking was employed to evaluate the binding affinity between ingredients and core targets. The expression levels of core targets were determined using qRT-PCR and ELISA.

Results: Psoriasis-related datasets GSE201827 and GSE174763 were comprehensively analyzed to obtain 635 psoriasis-related genes. These genes were further enriched to elucidate signaling pathways involved, leading to the construction of psoriasis pathogenic pathway network. Utilizing UPLC-MS/MS, 29 main ingredients of CQC were characterized. CQC ingredients-targets network was constructed using these ingredients and their targets. Screening of CQC anti-psoriasis core targets using machine learning algorithm. Molecular docking confirmed good binding affinity between these targets and ingredients. Imiquimod (IMQ) induced psoriasis-like rat validated the anti-psoriasis effect of CQC by alleviating symptoms, reducing spleen and thymus index, and modulating the expressions of core targets at mRNA and protein levels.

Conclusion: CQC effectively modulates the expression levels of AURKB, CCNB1, CCNB2, CCNE1, CDK1, and JAK3 through various ingredients, such as astilbin, salvianolic acid A, and engeletin, via multiple pathways, thereby alleviating psoriasis-like symptoms.

Keywords: compound qingdai capsule; machine learning; molecular mechanism; psoriasis; systemic pharmacology.

Graphical abstract

Figure 1

Multi-Omics and systems pharmacology analysis workflow on CQC anti-psoriasis core targets.

Figure 2

Acquisition of psoriasis-associated DEGs and DEMs and construction of the psoriasis pathogenic pathway network. (A) PCA plot showed sample distribution for both NL and LS groups in the GSE201827 dataset; (B) Volcano plot showed all differentially expressed genes screened from GSE201827 dataset. (C) PCA plot showed sample distribution for both NL and LS groups in the GSE174763 dataset; (D) Volcano plot showed all differentially expressed miRNAs screened from GSE174763 dataset. (E) KEGG pathway of psoriasis-related genes. (F) Network diagram of psoriasis pathogenic pathways.

Figure 3

Compositional analysis of CQC and obtaining their predicted targets. (A and B) Base peak chromatogram of Compound Qingdai Capsules in positive (A) and negative (B) ion modes. (C) Ingredients-Targets Network Diagram of CQC. Purple squares are main ingredients, red circles are targets.

Figure 4

Candidate targets of CQC anti-psoriasis, and PPI, GO and KEGG pathway enrichment. (A) Venn diagram showed crossover genes (CQC anti-psoriasis candidate targets) among DEMs-related genes, DEGs and CQC-related targets. (B) PPI network diagram of CQC anti-psoriasis candidate targets, and Hub-genes analyzed by cytoHubba. Node color is correlated with MCC score. The size of the node is positively correlated with the number of other nodes connected. (C) GO terms of CQC anti-psoriasis candidate targets. (D) KEGG pathways of CQC anti-psoriasis candidate targets.

Figure 5

Identification of CQC anti-psoriasis core targets by using Machine learning. (A–D) LASSO regression algorithm screen characteristic targets from CQC anti-psoriasis candidate targets. (A) Path diagram of the regression coefficients for model fitting on the training set: the upper X-axis represents number of independent variables, the lower X-axis represents Log Lambda, each line represents one target gene, and Y-axis represents the regression coefficients corresponding to target genes. (B) Trend plot of the number of variable screenings corresponding to Log(λ) in the cross-validation model: the upper X-axis represents the number of independent variables, the lower X-axis represents the Log(λ) value corresponding to the value of λ, and Y-axis represents the mean square error. (C) ROC plots for the optimal Lasso regression model, training set in red, test set in blue. (D) Histogram of correlation coefficients of characterized genes screened by Lasso regression algorithm. (E–G) RandomForests screen characteristic key targets from CQC anti-psoriasis candidate targets. (E) Random forest model training effect diagram: X-axis represents the number of decision trees in the random forest model and Y-axis represents the proportion of misclassified samples. (F) ROC plots for the optimal RandomForests, training set in red, test set in blue. (G) Scatterplot of key targets in the optimal model of RandomForests: X-axis represents the Mean Decrease Gini of the key target, ie the importance of the key target to the model, and Y-axis represents the key target. (H–L) SVM screen characteristic targets from CQC anti-psoriasis candidate targets. ROC plots of SVM models constructed with linear kernel (H), polynomial kernel (I), radial kernel (J) and sigmoid kernel (K), training set in red, test set in blue. (L) Trend plot of variables screened number against sample prediction accuracy in the optimal SVM model: X-axis represents the number of feature variables, Y-axis represents the sample prediction accuracy corresponding to that number of samples, red dot represents the results of the optimal SVM model. (M) Intersection genes among PPI, LASSO regression algorithm, RandomForests and SVM, the CQC anti-psoriasis core targets.

Figure 6

Validation of CQC anti-psoriasis core targets based on GSE201827 dataset. (A-F) ROC curves of AURKB (A), CCNB1 (B), CCNB2 (C), CCNE1 (D), CDK1 (E) and JAK3 (F). (G) Comparison of gene expression level of AURKB, CCNB1, CCNB2, CCNE1, CDK1, JAK3 between NL and LS groups. Data are represented as the mean ± SD (n: NL=70, LS=63) and t-tests were used for comparison of the significant differences. (H) Heatmap showed molecular docking results of core targets with CQC main ingredients. (I) The complex conformations charts for Astilbin with AURKB. (J) The complex conformations charts for Rosmarinic acid with CCNB1. (K) The complex conformations charts for Salvianolic acid A with CDK1.

Figure 7

Relief of psoriasis-like skin lesions in rats induced by IMQ through CQC treatment. (A) Rats experimental design. (B) Changes in rats’ back hair removal area in the six groups. (C–F) Trend graph of PASI scores in rats’ back hair removal area in the six groups, including scaling (C), erythema (D), infiltration (E) and PASI scores (F). Data are represented as the mean ± SEM (n = 6), on the day 8, each group was compared with the Con group (^#p < 0.05, ^##p < 0.01, ^###p < 0.001), on the day 15, the IMQ group was compared with the Con group (^#p < 0.05, ^##p < 0.01, ^###p < 0.001), other groups was compared with the IMQ group (*p < 0.05, **p < 0.01, ***p < 0.001). (G) HE staining of rat back skin tissue under digital scanning laser microscope at 100× magnification. Effect of CQC on spleen index (H) and thymus index (I) in six groups of rats, data are represented as the mean ± SD, and each group was compared with the IMQ group. (n = 6, *p < 0.05, **p < 0.01, ***p < 0.001).

Figure 8

Down-regulation in the mRNA levels of core targets during CQC intervention in psoriasis-like rats. Effects of CQC on the mRNA expression levels of AURKB (A), CCNB1 (B), CCNB2 (C), CCNE1 (D), CDK1 (E) and JAK3 (F) in the skin tissues of six groups of rats. Data are represented as the mean ± SD, and each group was compared with the IMQ group. (n = 6, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001).

Figure 9

Down-regulation in the protein levels of core targets during CQC intervention in psoriasis-like rats. Effects of CQC on the protein expression levels of AURKB (A), CCNB1 (B), CCNB2 (C), CCNE1 (D), CDK1 (E) and JAK3 (F) in the skin tissues of six groups of rats. Data are represented as the mean ± SD, and each group was compared with the IMQ group. (n = 6, *p < 0.05, **p < 0.01, and ***p < 0.001.