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Updated: Jun 4, 2025

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes
Published on: March 28, 2025
Metabolomic and transcriptomic analysis reveals metabolic-immune interactions in choroid neovascularization
Yihan Zhang1, Siyi Qi1, Weiai Shen1
1Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, 83 Fenyang Road, Shanghai, 200031, China; Key Laboratory of Myopia and Related Eye Diseases, NHC, Chinese Academy of Medical Sciences, 83 Fenyang Road, Shanghai, 200031, China; Shanghai Key Laboratory of Visual Impairment and Restoration, 83 Fenyang Road, Shanghai, 200031, China.
Abstract:
Choroid neovascularization (CNV) is a distinct type of age-related macular degeneration (AMD) with a poor prognosis and responsible for the majority of vision loss in the elderly population. The laser-induced CNV model is a well-established animal model frequently used to study CNV. In this study, we performed an integrated analysis of metabolomic and transcriptomic data from CNV samples, utilizing multiple approaches including single-sample gene set enrichment analysis (ssGSEA), correlation analysis, and weighted gene co-expression network analysis (WGCNA), alongside various bioinformatics platforms, to identify key metabolic and immune signatures and to investigate their interplay during angiogenesis. Dominant infiltration of macrophages and monocytes was detected and a positive correlation between dysregulated riboflavin metabolism and angiogenesis pathways was characterized. Hub genes such as ectonucleotide pyrophosphatase/phosphodiesterase 1 (Enpp1) and acid phosphatase 5, tartrate resistant (ACP5) emerged as potential central regulators of immune-metabolic crosstalk in CNV. The classification of the immune and metabolic landscape and their critical interactions in CNV models will enhance the understanding of the pathogenesis of neovascular AMD and other neovascular eye diseases, contributing to the development of multi-targeted therapeutic strategies with better efficacy.

