Mendelian Randomization Transcriptomics and Network Pharmacology for Identification of Osteoarthritis Drug Targets

Summary

We present a methodological protocol that integrates Mendelian randomization, transcriptomic analysis, and network pharmacology to systematically identify candidate drug targets for osteoarthritis. This approach enables the prioritization of targets through genetic, expression, and interaction evidence, using publicly available data and computational tools.

Abstract

This article delineates an integrated methodology that combines Mendelian randomization (MR), transcriptomic analysis, and network pharmacology to identify and prioritize potential therapeutic targets for osteoarthritis (OA). It is designed to guide researchers in implementing this multimodal pipeline to investigate drug repurpose and the development of novel therapeutic interventions for OA. The methodological framework comprises five sequential stages: first, MR analysis pipeline, employing two-sample MR to identify putative causal plasma proteins associated with OA, followed by Steiger filtering and phenome-wide association scanning to assess causal directionality and potential off-target effects; second, transcriptomic sequencing workflow, integrating RNA-seq data to identify and refine candidate protein targets; third, integration strategy, merging MR and transcriptomic results to prioritize candidate proteins; fourth, network pharmacology and molecular docking procedures, involving the construction of protein-protein interaction networks, functional enrichment analysis, and molecular docking to explore ligand-target interactions; and fifth, intended application, focusing on the prioritization of candidate compounds and natural products with potential therapeutic relevance. By organizing the workflow into distinct analytical phases, this framework provides a reproducible approach for transitioning from genetic and transcriptomic discovery to computational drug-target evaluation, without presenting specific experimental outcomes. The methodology facilitates systematic and hypothesis-driven investigation into OA therapeutics using publicly accessible datasets and computational tools.

Introduction

Osteoarthritis (OA) is characterized by the progressive deterioration of cartilage, changes in subchondral bone, and excessive bone growth¹. It is now a major cause of disability and economic burden worldwide². Current treatments primarily involve steroidal or nonsteroidal anti-inflammatory drugs (NSAIDs) to alleviate pain and inflammation. However, NSAIDs often lead to undesirable side effects in the gastrointestinal and cardiovascular systems³. Joint replacement surgery offers an alternative, but approximately one-third of patients do not experience significant pain relief or functional improvem....

Protocol

We obtained ethical approval and informed consent from the Biomedical Research Ethics Committee of the First Affiliated Hospital of Nanchang University. Ethics Number: (2025)CDYFYYLK(08-007).

MR analysis

Data retrieval
The plasma pQTL data were obtained from the study by Zheng et al.¹⁴, which integrated five GWAS datasets^{15,16,17,18,19}, and from the study by Ferkingstad et al. The....

Results

Screening the proteome for osteoarthritis causal proteins

Using plasma pQTL data from the study by Zheng et al., Mendelian randomization (MR) analysis identified six protein-knee or hip OA associations, one protein-knee OA association, and four protein-hip OA associations that met the Bonferroni significance threshold (P < 5.63 × 10⁻⁵) (Table 1 and Figur.......

Discussion

This is a two-sample MR analysis method that has been employed to predict potential proteins related to osteoarthritis (OA), supplemented by sequencing validation of blood samples from OA patients and network pharmacology to predict drug targets for OA. We utilized plasma proteomics data and GWAS data for OA to conduct the two-sample MR analysis, identifying 19 proteins associated with OA, including EFEMP1, PTHLH, PCSK1, ECM1, ISLR2, SEMA3G, SPOCK2, LRIG3, TMEM190, CRYZ, KNG1, OMG, DDX19A, MAPK3, PTPN9, USP8, DDX19B, PRO.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Agilent 2100 Bioanalyzer	Agilent Technologies	G2939AA	Automated RNA quality assessment system
AutoDock Vina	The Scripps Research Institute	Vina 1.1.2	Open-source command-line software for computational molecular docking
Illumina NovaSeq 6000 platform	Illumina	SY-415-1001	High-throughput next-generation sequencing platform
NEBNext Ultra RNA Library Prep Kit	New England Biolabs	NEB #E7770	Fragment and transcribe enriched mRNA into cDNA
Ribo-Zero Magnetic Kit	Epicentre	MRZH11124	Eliminate rRNA
Trizol reagent kit	Invitrogen	AM9738	Extract total RNA
UCSF Chimera	University of California, San Francisco	Chimera-alpha-win64	Molecular visualization and structure analysis software.