Observational analysis showed dose-dependent modulation of FOX transcription factors in human PBMCs, indicating potential for age-related disease treatment.
Background: Forkhead box (FOX) transcription factors constitute a large family of regulatory proteins that control diverse cellular processes, including development, metabolism, immunity, and aging. Metadichol, a nano lipid formulation derived from long-chain alcohols, has demonstrated pleiotropic biological effects, including immunomodulation and metabolic regulation. Objective: To comprehensively evaluate the effects of metadichol treatment on FOX transcription factor gene expression in human peripheral blood mononuclear cells (PBMCs) via quantitative PCR analysis. Methods: Human PBMCs were isolated via Histopaque density gradient centrifugation and treated with Metadichol atconcentrations of 1 pg/ml, 100 pg/ml, 1 ng/ml, and 100 ng/ml. Total RNA was extracted, reverse-transcribed, andanalyzed by quantitative PCR for 45 FOX genes. Gene expression changes were calculated via normalization to GAPDH via the 2^-ΔΔCq method. Results: Metadichol treatment resulted in dose-dependent modulation of FOX gene expression. At the highest concentration (100 ng/ml), significant upregulation of multiple FOX genes was observed, with FOXO1 showing thegreatest increase (8.74-fold), followed by FOXA1 (7.39-fold) and FOXH1 (7.22-fold). Additional substantial increases were noted for FOXA2 (6.57-fold), FOXA3 (6.98-fold), FOXB1 (6.79-fold), FOXP3 (5.46-fold), andFOXP4 (6.23-fold). Conversely, selective downregulation was observed for FOXL2 (0.16-fold), FOXL1 (0.54-fold), and FOXD4L1 (0.56-fold). Conclusions: Metadichol has potent and selective effects on FOX transcription factor expression in human PBMCs, with preferential upregulation of genes involved in metabolic regulation, immune homeostasis, and cellular longevity pathways. These findings suggest potential therapeutic applications in age-related diseases, metabolic disorders, andimmunomodulation. The differential expression patterns indicate complex regulatory mechanisms that warrant furtherinvestigation to elucidate their clinical translation potential.
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Palayakotai Raghavan (2025) studied this question.
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