Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network

Exploring the Mechanism of Wenshen Huatan Quyu Decotion for PCOS Based on Network Pharmacology and Molecular Docking Verification

Objective

To identify the active chemical in Wenshen Huatan Quyu Decotion (WHQD) and to explore its possible network interactions with the polycystic ovary syndrome (PCOS).

Methods

The Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP) and the Bioinformatics Analysis Tool for Molecular Mechanisms in Chinese Medicine (BATMAN-TCM) were used to decompose compound formulations, detect active chemicals and their corresponding target genes, and then convert them into UniProt gene symbols. Meanwhile, PCOS-related target genes were collected from GeneCards to construct a protein-protein interaction (PPI) network, which was further analyzed by STRING online database. Gene Ontology (GO) functional analysis was also performed afterwards to construct the component-target gene-disease network to visualize the correlation between WHQD and PCOS. We then performed an in silico molecular docking study to validate the predicted relationships.

Results

WHQD consists of 14 single drugs containing a total of 67 chemical components. 216 genes were predicted as possible targets. 123 of the 216 target genes overlapped with PCOS. GO annotation analysis revealed that 1968 genes were associated with biological processes, 145 with molecular functions, and 71 with cellular components. KEGG analysis revealed 146 pathways involved PPI, and chemical-target gene-disease networks suggest that PGR, AR, ADRB2, IL-6, MAPK1/8, ESR1/2, CHRM3, RXRA, PPARG, BCL2/BAX, GABRA1, and NR3C2 may be key genes for the pharmacological effects of WHQD on PCOS. Molecular docking analysis confirmed that hydrogen bonding was the main interaction between WHQD and its targets.

Conclusion

WHQD exerts its pharmacological effects by improving insulin sensitivity, subfertility, and hormonal imbalance, increasing ovulation rates, which in turn may increase pregnancy rates in patients with significant efficacy.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Repurposing new drug candidates and identifying crucial molecules underlying PCOS Pathogenesis Based On Bioinformatics Analysis

BACKGROUNDS

Polycystic ovary syndrome affects 7% of women of reproductive ages. Poor-quality oocytes, along with lower cleavage and implantation rates, reduce fertilization.

OBJECTIVE

This study aimed to determine crucial molecular mechanisms behind PCOS pathogenesis and repurpose new drug candidates interacting with them. To predict a more in-depth insight, we applied a novel bioinformatics approach to analyze interactions between the drug-related and PCOS proteins in PCOS patients.

METHODS

The newest proteomics data was retrieved from 16 proteomics datasets and was used to construct the PCOS PPI network using Cytoscape. The topological network analysis determined hubs and bottlenecks. The MCODE Plugin was used to identify highly connected regions, and the associations between PCOS clusters and drug-related proteins were evaluated using the Chi-squared/Fisher's exact test. The crucial PPI hub-bottlenecks and the shared molecules (between the PCOS clusters and drug-related proteins) were then investigated for their drug-protein interactions with previously US FDA-approved drugs to predict new drug candidates.

RESULTS

The PI3K/AKT pathway was significantly related to one PCOS subnetwork and most drugs (metformin, letrozole, pioglitazone, and spironolactone); moreover, VEGF, EGF, TGFB1, AGT, AMBP, and RBP4 were identified as the shared proteins between the PCOS subnetwork and the drugs. The shared top biochemical pathways between another PCOS subnetwork and rosiglitazone included metabolic pathways, carbon metabolism, and citrate cycle, while the shared proteins included HSPB1, HSPD1, ACO2, TALDO1, VDAC1, and MDH2. We proposed some new candidate medicines for further PCOS treatment investigations, such as copper and zinc compounds, reteplase, alteplase, gliclazide, Etc.

CONCLUSION

Some of the crucial molecules suggested by our model have already been experimentally reported as critical molecules in PCOS pathogenesis. Moreover, some repurposed medications have already shown beneficial effects on infertility treatment. These previous experimental reports confirm our suggestion for investigating our other repurposed drugs (in vitro and in vivo).

Genetic polymorphisms associated with polycystic ovary syndrome among Iranian women

Polycystic ovary syndrome (PCOS) involves abnormalities in ovarian, reproductive, and metabolic systems. Genetic polymorphisms associated with individual differences and variations might be related to complex disorders with unknown causes, including PCOS. Several leading genetic markers with known cellular functions have been identified among Iranian women presenting with PCOS. In particular, the existing evidence shows a significant relationship between PCOS and the following genetic polymorphisms: rs2275913 (interleukin-17A), rs9927163 (interleukin-32), Pro12Ala (peroxisome proliferator-activated receptor-γ), rs17173608 (chemerin), rs2236242 (vaspin), ApaI (vitamin D receptor), and rs7895833 (sirtuin 1). In addition, a higher risk of PCOS is associated with the rs2910164 (microRNA 146a), rs2241766 (adiponectin), -34 T/C (cytochrome 17), and rs1800682 (Fas) polymorphisms. Furthermore, protective effects against PCOS have been reported for the A4223C polymorphism of adenosine deaminase 1. Overall, the available data indicate that Iranian women with PCOS have a higher prevalence of polymorphisms in inflammation- and metabolism-related genes, but not in insulin-related genes. More extensive studies are needed to identify the ethnicity-related genetic associations in PCOS.

Identification of key genes and pathways and therapeutic agents in cadmium-treated liver cells: A bioinformatics study

Evidence indicates that Cadmium (Cd) can accumulate in liver, which results in acute or chronic cell damage with unclear complex mechanisms. Thus, we aimed to explore the possible molecules and pathways by using bioinformatics methods Consequently, two datasets (GSE8865 and GSE31286) were retrieved and the differentially expressed genes (DEGs) were screened out. The intersection of the DEGs included seven up-regulated and forty-three down-regulated genes, which were mainly enriched in biological cell proliferation items, and were enriched in several metabolism-related pathways. Among the DEGs, several hub genes such as EGR1, FOSL1, ITGA2, EDN1, and IER3 were screened out through protein-protein interaction analysis. Interestingly, BW-B70C was predicted to be a potential agent for attenuating Cd-induced liver cell damage. The present study gave a novel insight into the mechanisms of Cd-induced liver cell damage or malignant transformation and identified several small agents that might be critical for Cd toxicity prevention and treatment.

Network-Wide Screen Identifies Variation of Novel Precise On-Module Targets Using Conformational Modudaoism

Modular targeting is promising in drug research at the network level, but it is challenging to quantificationally identify the precise on-modules. Based on a proposed Modudaoism (MD), we defined conserved MD (MDc) and varied MD (MDv) to quantitatively evaluate the conformational and energy variations of modules, and thereby identify the conserved and discrepant allosteric modules (AMs). Compared to the Z_summary , MDc/MDv got an optimized result of module preserved ratio and modular structure. In the mice anti-ischemic networks, 3, 5, and 1 conserved AMs as well as 4, 1, and 3 on-modules of baicalin (BA), jasminoidin (JA), and ursodeoxycholic acid (UA) were identified by MDc and MDv, 5 unique AMs and their characteristic actions were revealed. Besides, co-immunoprecipitation (Co-IP) experiments validated the representative modular structure. MDc/MDv method can quantitatively define the conformational variations of modules and screen the precise on-modules network-wide, which may provide a promising strategy for drug discovery.