Examining the Effects of Nutrient Supplementation on Metabolic Pathways via Mitochondrial Ferredoxin in Aging Ovaries

Abstract

As women age, oocytes are susceptible to a myriad of dysfunctions, including mitochondrial dysfunction, impaired DNA repair mechanisms, epigenetic alterations, and metabolic disturbances, culminating in reduced fertility rates among older individuals. Ferredoxin (FDX) represents a highly conserved iron-sulfur (Fe-S) protein essential for electron transport across multiple metabolic pathways. Mammalian mitochondria house two distinct ferredoxins, FDX1 and FDX2, which share structural similarities and yet perform unique functions. In our investigation into the regulatory mechanisms governing ovarian aging, we employed a comprehensive multi-omics analysis approach, integrating spatial transcriptomics, single-cell RNA sequencing, human ovarian pathology, and clinical biopsy data. Previous studies have highlighted intricate interactions involving excessive lipid peroxide accumulation, redox-induced metal ion buildup, and alterations in cellular energy metabolism observed in aging cells. Through a multi-omics analysis, we observed a notable decline in the expression of the critical gene FDX1 as ovarian age progressed. This observation prompted speculation regarding FDX1's potential as a promising biomarker for ovarian aging. Following this, we initiated a clinical trial involving 70 patients with aging ovaries. These patients were administered oral nutritional supplements consisting of DHEA, ubiquinol CoQ10, and Cleo-20 T3 for a period of two months to evaluate alterations in energy metabolism regulated by FDX1. Our results demonstrated a significant elevation in FDX1 levels among participants receiving nutritional supplementation. We hypothesize that these nutrients potentiate mitochondrial tricarboxylic acid cycle (TCA) activity or electron transport chain (ETC) efficiency, thereby augmenting FDX1 expression, an essential electron carrier in metabolic pathways, while concurrently mitigating lipid peroxide accumulation and cellular apoptosis. In summary, our findings underscore the potential of nutritional intervention to enhance in vitro fertilization outcomes in senescent cells by bolstering electron transport proteins, thus optimizing energy metabolism and improving oocyte quality in aging women.

Keywords: FDX1; multi-omics; nutrients; ovarian aging.

Figure 1

The flowchart serves as a visual representation of the systematic approach followed in conducting the research and ensuring the selection of appropriate studies for inclusion in the analysis.

Figure 2

Spatial transcriptomics reveals characteristics of distinct cell clusters in young and aged mouse ovaries. Spatial transcriptomics permits in-depth examination of tissue sections, facilitating the identification and precise alignment of clusters. This method also offers insights into morphological features and diverse cell clusters, as evidenced by their correlation with H&E staining (A). Each color in the representation signifies a distinct cell cluster (B). (C) The dataset includes essential features such as total counts and gene counts, providing crucial information for comprehensive analysis and interpretation.

Figure 3

Spatial transcriptomics unveils genetic changes in FDX1 within the mouse ovary. (A) UMAP visualization showcases cells from young and aged ovaries, with color codes representing predominant cell types. (B) A pie chart illustrates the distribution of different cell populations within the ovarian microenvironment. (C) The heat map displays the gene expression levels and spatial distribution of FDX1 across ovaries from mice of varying ages. (D) Dot plots illustrate the expression patterns of FDX1 across distinct clusters. (E) Violin plots depict FDX1 transcript abundance in ovarian regions of young and aged mice.

Figure 4

CellChat investigates intercellular communication networks among different cell types. Spatial transcriptomics identifies intercellular communication within ovarian tissue and evaluates variations in signaling pathways related to SEMA3 (A), VEGF (B), BMP (C), ANGPTL (D) and, across different cell types, along with network centrality scores.

Figure 5

Supplementation influences the metabolic reprogramming of cumulus cells in aging ovarian patients. (A) QPCR analysis of FDX1 gene levels. Metabolic pathway diagram of glycolysis (B) and TCA cycle (C). * p < 0.05, ** p < 0.01, and *** p < 0.001.