Exploring the potential of gut microbiota metabolites in the treatment of endometriosis through network pharmacology and Mendelian randomization

Jun Zhou, Tiehu Shan, Li Y, Guangmei Zhang
In: Frontiers in Microbiology · 2026 · vol. 17 · doi:10.3389/fmicb.2026.1733323 · W7164417728
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Abstract

Background Studies have shown that dysregulation of the gut microbiota (GM) plays a crucial role in the development of endometriosis (EMs). Study aimed to investigate the potential of protective GM metabolites in treating EM through network pharmacology and Mendelian randomization (MR), opening new avenues for targeted therapeutic strategies. Methods All data were sourced from publicly available databases. Biomarkers linked to protective GM metabolites in EMs were identified employing MR study (with GM as the exposure and EMs as the outcome), differential expression analysis, machine learning, and gene expression analyses. Key metabolites associated with these biomarkers were identified through the gutMGene database. A network connecting biomarkers, key metabolites, and key microbes was constructed. Subsequently, drug similarity for the key metabolites was assessed, and molecular docking studies were performed to evaluate potential therapeutic interactions. Finally, reverse transcription quantitative PCR (RT-qPCR) analyses were conducted to further investigate the expression of biomarkers in clinical samples. Results The MR study identified 5 key protective microbiota (such as genus. Bifidobacterium .id.436) with a causal relationship to EMs (OR < 1, 95% CI ≠ 1, p < 0.05). After, AOC3, FABP4, and NEK2 were identified as biomarkers, with AOC3 and FABP4 showing low expression in EMs samples and NEK2 showing higher expression. Based on these biomarkers, 4 key metabolites were identified, and a biomarker-key metabolite-key microbe network was constructed, illustrating relationships such as AOC3-N-acetylputrescine- Bifidobacterium . The key metabolites interacting with biomarkers adhere to Lipinski’s rules for pharmacokinetic properties. Molecular docking revealed the binding affinity between biomarkers and key metabolites, with a binding energy of −6.6 kcal/mol for NEK2 and 6-[(4R,5S)-5-methyl-2-oxoimidazolidin-4-yl]hexanoic acid. Finally, RT-qPCR analysis confirmed that the expressions of AOC3 were significantly higher in normal samples compared to case samples (both ectopic and eutopic), while FABP4 expression was significantly higher in normal samples only when compared to eutopic samples. Conclusion This study identified AOC3, FABP4, and NEK2 as potential EMs biomarkers associated with gut microbiota-derived metabolites. However, the proposed microbiota-metabolite-biomarker network requires further experimental validation.

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