Renshen Yangrong decoction for secondary malaise and fatigue: network pharmacology and Mendelian randomization study

Abstract

Background: Renshen Yangrong decoction (RSYRD) has been shown therapeutic effects on secondary malaise and fatigue (SMF). However, to date, its bioactive ingredients and potential targets remain unclear.

Purpose: The purpose of this study is to assess the potential ingredients and targets of RSYRD on SMF through a comprehensive strategy integrating network pharmacology, Mendelian randomization as well as molecular docking verification.

Methods: Search for potential active ingredients and corresponding protein targets of RSYRD on TCMSP and BATMAN-TCM for network pharmacology analysis. Mendelian randomization (MR) was performed to find therapeutic targets for SMF. The eQTLGen Consortium (sample sizes: 31,684) provided data on cis-expression quantitative trait loci (cis-eQTL, exposure). The summary data on SMF (outcome) from genome-wide association studies (GWAS) were gathered from the MRC-IEU Consortium (sample sizes: 463,010). We built a target interaction network between the probable active ingredient targets of RSYRD and the therapeutic targets of SMF. We next used drug prediction and molecular docking to confirm the therapeutic value of the therapeutic targets.

Results: In RSYRD, network pharmacology investigations revealed 193 possible active compounds and 234 associated protein targets. The genetically predicted amounts of 176 proteins were related to SMF risk in the MR analysis. Thirty-seven overlapping targets for RSYRD in treating SMF, among which six (NOS3, GAA, IMPA1, P4HTM, RB1, and SLC16A1) were prioritized with the most convincing evidence. Finally, the 14 active ingredients of RSYRD were identified as potential drug molecules. The strong affinity between active components and putative protein targets was established by molecular docking.

Conclusion: This study revealed several active components and possible RSYRD protein targets for the therapy of SMF and provided novel insights into the feasibility of using Mendelian randomization for causal inference between Chinese medical formula and disease.

Keywords: Mendelian randomization; Renshen Yangrong decoction; molecular docking; network pharmacology; secondary malaise and fatigue.

Figure 1

Overview of the study design.

Figure 2

The forest plot displays the results of 37 candidate druggable genes.

Figure 3

Manhattan plot of Mendelian randomization analysis. The blue line represents the nominal significant threshold of 0.05. The red line represents the false discovery rate threshold of 0.05.

Figure 4

Target genes for RSYRD treatment of SMF. (A) Venn diagram of RSYRD target genes and SMF druggable genes. (B) Volcano plot showing the results of proteome-wide Mendelian randomization.

Figure 5

Sensitivity analysis of druggable genes on secondary malaise and fatigue. (A) GAA, (B) IMPA1, (C) NOS3, (D) P4HTM, (E) RB1, (F) SLC16A1.

Figure 6

The network of herb-ingredient-target includes 12 kinds of herbs, 86 active ingredients, and 37 target genes.

Figure 7

The network of herb-ingredient-target includes 12 kinds of herbs, 32 active ingredients, and 6 target genes.

Figure 8

GO and KEGG pathway enrichment. (A) GO and (B) KEGG pathway enrichment of 17 druggable genes of SMF. (C) KEGG pathway enrichment of 6 prospective therapeutic target genes.

Figure 9

Heatmaps of the docking scores of target genes combined with active ingredients of RSYRD. The 3D structure of target genes was displayed.

Figure 10

Molecular docking of target genes with active ingredients of RSYRD. (A) Ferulic acid docking RB1. (B) Curcumin docking RB1. (C) Quercetin docking RB1. (D) Oleic acid docking RB1. (E) Acteoside docking RB1. (F) Curcumin docking NOS3. (G) Quercetin docking NOS3. (H) Isorhamnetin docking NOS3. (I) Benzoic acid docking NOS3. (J) Oleic acid docking NOS3. (K) Chlorogenic acid docking NOS3. (L) Ginsenoside Re docking NOS3. (M) Quercetin docking SLC16A1. (N) Benzoic acid docking SLC16A1. (O) Naringenin docking SLC16A1. (P) Pyruvic acid docking SLC16A1. (Q) Quercetin docking GAA. (R) Rutin docking GAA. (S) Isoquercitrin docking GAA. (T) (−)-Epicatechin docking GAA.

Figure 11

SMF mRNA Expression analysis using GEO Dataset. (A) Principal component analysis for GSE12385. (B) Volcano of the differentially expressed genes. The red dots represent the significantly up-regulated genes and the blue suggest the significantly down-regulated genes. (C) Heatmap of the 6 key genes in blood samples at 2 time points. The box plot of the GAA (D), SLC16A1 (E), and P4HTM (F) in the GSE12385 dataset.