Deciphering the Transcriptional Metabolic Profile of Adipose-Derived Stem Cells During Osteogenic Differentiation and Epigenetic Drug Treatment

Abstract

Adipose-derived mesenchymal stem cells (ASCs) are commonly employed in clinical treatment for various diseases due to their ability to differentiate into multi-lineage and anti-inflammatory/immunomodulatory properties. Preclinical studies support their use for bone regeneration, healing, and the improvement of functional outcomes. However, a deeper understanding of the molecular mechanisms underlying ASC biology is crucial to identifying key regulatory pathways that influence differentiation and enhance regenerative potential. In this study, we employed the NanoString nCounter technology, an advanced multiplexed digital counting method of RNA molecules, to comprehensively characterize differentially expressed transcripts involved in metabolic pathways at distinct time points in osteogenically differentiating ASCs treated with or without the pan-DNMT inhibitor RG108. In silico annotation and gene ontology analysis highlighted the activation of ethanol oxidation, ROS regulation, retinoic acid metabolism, and steroid hormone metabolism, as well as in the metabolism of lipids, amino acids, and nucleotides, and pinpointed potential new osteogenic drivers like AOX1 and ADH1A. RG108-treated cells, in addition to the upregulation of the osteogenesis-related markers RUNX2 and ALPL, showed statistically significant alterations in genes implicated in transcriptional control (MYCN, MYB, TP63, and IRF1), ethanol oxidation (ADH1C, ADH4, ADH6, and ADH7), and glucose metabolism (SLC2A3). These findings highlight the complex interplay of the metabolic, structural, and signaling pathways that orchestrate osteogenic differentiation. Furthermore, this study underscores the potential of epigenetic drugs like RG108 to enhance ASC properties, paving the way for more effective and personalized cell-based therapies for bone regeneration.

Keywords: DNA methyltransferase inhibitor; adipose-derived stem cells; metabolic pathways; osteogenic differentiation.

Figure 1

RG108 treatment in ASC morphology and proliferation. (A) Phase-contrast microphotographs showing ASCs treated with RG108 or DMSO (as mocked control) for 72 h in culturing medium. Scale bars represent 400 μm. Representative images of three (n = 3) independent experiments. (B) Proliferation of ASCs treated with RG108 or DMSO for 72 h using Trypan blue exclusion dye. Bars are median values of three (n = 3) independent experiments.

Figure 2

RG108 positively affects ASC osteogenic differentiation. (A) Phase-contrast microphotographs showing ASCs treated with RG108 or DMSO (as mocked control) at days 0, 7, 14, and 21 after osteogenic induction. Scale bars: 200 μm. (B) Phase-contrast microphotographs depicting mineralization (intense red clusters) in ASCs treated with RG108 or DMSO and stained with Alizarin Red at 14- and 21-day time points of osteogenic induction. Scale bars: 400 μm. (C) mRNA expression of osteogenic markers RUNX2, ALPL, and COL1A1 was evaluated by qRT-PCR. Data were normalized to GAPDH mRNA expression. Bars represent means ± SD of three (n = 3) independent experiments, each performed in triplicate. * p < 0.05 and ** p < 0.01 vs. T0 control; ^# p < 0.05 vs. T7 DMSO; ^$ p < 0.05 vs. T21 DMSO. (D) Western blot analysis of ALPL protein expression in ASCs induced toward osteogenesis with or without RG108 treatment. Hsp90 was used as internal control. Densitometric analysis of ALPL expression is shown as relative expression compared to the T0 control sample.

Figure 3

Transcriptional changes during osteogenesis differentiation. (A) Correlation analysis of log2 fold changes at reported time points (T7, T14, and T21) compared to T0. Red dots indicate upregulated genes, while blue dots represent downregulated genes. Dashed lines indicate log2 fold change thresholds, set at +1 and −1. (B) Cluster heatmap of upregulated and downregulated genes at distinct time points (T0, T7, T14, and T21) identified in (A). Expression values are reported as Z-score. (C) River plot of metabolic gene set enrichment analysis (REACTOME v2024.1) of upregulated genes. The gene ratio of upregulated genes to total genes in the different pathways is reported. Significance is reported as −log₁₀ (FDR) and indicated with color gradient.

Figure 4

River plots of upregulated genes (A) and downregulated (B) genes related to non-metabolic processes during ASC osteogenic differentiation.

Figure 5

NanoString result validation by qRT-PCR. (A) qRT-PCR results of selected upregulated and (B) downregulated genes across ASC osteogenic differentiation compared to the T0 control sample. Transcript levels were normalized to GAPDH mRNA expression. Bars represent means ± SD of three (n = 3) independent experiments, each performed in triplicate. * p < 0.05; ** p < 0.01; *** p < 0.001 vs. T0 control sample.

Figure 6

Transcriptional changes during osteogenesis differentiation of ASCs treated with RG108. (A) Correlation analysis of log2 fold changes in ASCs treated or not with RG108 at reported time points (T7, T21) compared to T0. Red dots indicate upregulated genes, blue dots indicate downregulated genes. Dotted lines indicate log2 fold change thresholds set at +1 and −1. (B) Cluster heatmap of upregulated and downregulated genes after RG108 treatment at distinct time points (T0, T7, and T21) identified in (A). Expression values are reported as Z-score. (C) Graph bars of enriched pathways for RG108 T7-upregulated genes calculated with Metascape tool. X axis shows significance reported as −log10 p value (p). (D) Graph bars of enriched pathways for RG108 T21-upregulated genes calculated with Metascape tool. X axis shows significance reported as −log10 p value (p).

Figure 7

DNA methylation profiles of genes deregulated by RG108 treatment during ASC osteogenesis. (A) DNA methylation array data from four different subcutaneous adipose tissues showing methylation status of genes involved in ethanol oxidation (ADH1C, ADH4, ADH6, and ADH7) regulated by RG108. (B) DNA methylation status of transcription factors (MYCN, MYB, TP63, and IRF1) whose expression is upregulated by RG108 treatment. (C) DNA methylation profile of HLA-DQA1. (D) DNA methylation status of SLC2A3. Data are reported as methylation scores in a range from 0 to 1, where 1 indicates a fully methylated site while 0 indicates a fully demethylated site.