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Metadichol Orchestrates Cellular Reprogramming and Regenerative Pathways via FOX Transcription Factor Networks: Implications for Immune–Metabolic Rejuvenation

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18 August 2025

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20 August 2025

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Abstract
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.
Keywords: 
Fox family
;  
Metadichol
;  
immune metabolic rejuvenation
;  
nuclear receptors
;  
SOX family
;  
Toll-like receptors
;  
KLFs
;  
sirtuins
;  
circadian genes
;  
GDF11
;  
TERT
;  
Klotho
;  
induced pluripotency
;  
regenerative medicine
Subject: 
Biology and Life Sciences  -   Life Sciences

Introduction

The Forkhead box (FOX) transcription factor superfamily comprises more than 50 members [1] organized into 19 subfamilies (FOXA-FOXS) [2,3] that regulate diverse cellular processes, including development [4], metabolism [5], immunity [6], and aging [7,8]. These evolutionarily conserved proteins are characterized by a shared forkhead DNA-binding domain [9] and exhibit tissue-specific expression patterns with distinct functional roles [10,11]. The clinical importance of FOX transcription factors has been extensively documented in the fields of cancer biology [12], metabolic diseases [13], autoimmune disorders [14], and aging-related pathologies [15,16].
Table 1. Comprehensive FOX Gene Function and Disease Association Table.
Table 1. Comprehensive FOX Gene Function and Disease Association Table.
FOX Gene Biological Process Disease Association Key Functions References
FOXA1 Organogenesis, hepatic development, pioneer transcription factor activity Prostate cancer, breast cancer, metabolic disorders Liver specification, pancreatic development, nuclear receptor cofactor [17,18]
FOXA2 Endodermal organ development, glucose homeostasis Type 2 diabetes, pancreatic disorders, lung development defects Pancreatic β-cell function, gluconeogenesis regulation, respiratory development [19,20]
FOXA3 Hepatic gene expression, metabolism Cholangiocarcinoma, liver cancer, metabolic syndrome Liver development, metabolic gene regulation, bile acid synthesis [21]
FOXB1 Neural development, cell proliferation Glioblastoma, neural tube defects Brain development, neural differentiation [22]
FOXD1 Kidney development, epithelial–mesenchymal transition Pancreatic cancer, renal disorders Kidney morphogenesis, EMT regulation, cancer metastasis [23,24]
FOXD2 Neural crest development Developmental disorders Neural crest cell migration, cranial development [25,26,27]
FOXD3 Neural crest development, stem cell maintenance Melanoma, developmental disorders Neural crest specification, stem cell pluripotency [28,29]
FOXD4 Embryonic development Unknown pathological significance Early development, recently duplicated in humans [30]
FOXE1 Thyroid development, neural development Thyroid cancer, congenital hypothyroidism Thyroid morphogenesis, neural tube closure [31,32,33]
FOXF1 Mesenchymal development, lung development Alveolar capillary dysplasia, lung disorders Lung development, angiogenesis [34,35]
FOXF2 Kidney development, angiogenesis Renal disorders, vascular malformations Kidney morphogenesis, vascular development [36,37]
FOXG1 Brain development, telencephalon formation Autism spectrum disorders, Rett syndrome-like phenotype Forebrain development, neurogenesis [38,39]
FOXH1 Mesoderm formation, nodal signaling Developmental disorders, cardiac defects Gastrulation, heart development, TGF-β signaling [40]
FOXJ1 Ciliogenesis, respiratory epithelium Primary ciliary dyskinesia, respiratory infections Cilia formation, respiratory function [41]
FOXJ2 Cell cycle regulation Cancer G2/M transition, DNA damage response [41]
FOXJ3 Cell cycle progression Cancer progression Mitotic regulation, chromosome segregation [41]
FOXK1 Muscle development, cell cycle Muscular disorders, cancer Myogenesis, proliferation control [42]
FOXK2 Muscle differentiation, metabolism Metabolic disorders, muscle diseases Skeletal muscle development, glucose metabolism [43]
FOXL1 Gastrointestinal development Gastrointestinal cancers Intestinal development, GI tract homeostasis [44]
FOXL2 Ovarian development, granulosa cell function Ovarian cancer, premature ovarian failure Ovarian follicle development, sex determination [45]
FOXM1 Cell cycle progression, DNA repair, mitosis Multiple cancers, aging-related diseases G1/S transition, M-phase progression, genomic stability [46]
FOXN1 Thymic development, hair follicle formation Severe combined immunodeficiency, alopecia T-cell development, skin differentiation [47]
FOXN2 Neural development Neurodevelopmental disorders Brain development, neuronal differentiation [48]
FOXN3 Cell cycle regulation, DNA damage response Cancer, aging Cell cycle checkpoints, DNA repair [49]
FOXN4 Retinal development Retinal disorders, blindness Retinal neurogenesis, photoreceptor development [50]
FOXO1 Glucose homeostasis, stress response, apoptosis Type 2 diabetes, cancer, metabolic syndrome Gluconeogenesis, insulin sensitivity, cellular stress response [51]
FOXO3 Aging, stress resistance, apoptosis Cancer, neurodegenerative diseases, longevity Oxidative stress response, longevity pathways, apoptosis [52]
FOXO4 Cell cycle arrest, DNA damage response Cancer, premature aging p21 induction, senescence, DNA repair [53]
FOXO6 Brain function, glucose metabolism Alzheimer's disease, diabetes Memory consolidation, hepatic gluconeogenesis [54]
FOXP1 B-cell development, cardiac morphogenesis Diffuse large B-cell lymphoma, intellectual disability B-cell differentiation, heart valve development [55]
FOXP2 Language development, neural function Speech and language disorders, autism Speech acquisition, motor learning, synaptic plasticity [56]
FOXP3 Regulatory T-cell function, immune tolerance Autoimmune diseases, IPEX syndrome, cancer Treg development, immune suppression, self-tolerance [57]
FOXP4 T-cell development, cardiac function Developmental disorders, cardiac defects T-cell differentiation, heart development [58]
FOXQ1 Epithelial development Colorectal cancer, gastric cancer Epithelial homeostasis, EMT regulation [59]
FOXR1 Neural development Cancer Brain development, cell proliferation [60,61]
FOXR2 Neural function Cancer Neural development, transcriptional regulation [62,63]
FOXS1 Neural crest development Developmental disorders Cranial neural crest formation [64]
Among the most studied FOX subfamilies, FOXA proteins function as pioneer transcription factors that facilitate chromatin remodeling and gene accessibility. [65,66] FOXA1, FOXA2, and FOXA3 are critical regulators of hepatic metabolism [67], pancreatic β-cell function [68], and lipid homeostasis.69 The FOXO subfamily, comprising FOXO1, FOXO3, FOXO4, and FOXO6, serves as key mediators of cellular stress responses [70] longevity pathways [71], and metabolic homeostasis [72] FOXP proteins, particularly FOXP3, are essential for regulatory T-cell development and immune tolerance. [73,74]
Metadichol is a novel nanolipid formulation consisting of long-chain alcohols derived from sugarcane [75]. Previous investigations have demonstrated that metadichol functions as a vitamin D receptor (VDR) agonist, modulates immune responses [76] and exhibits antiviral properties [77,78,79]. This compound has been shown to increase endogenous vitamin C levels. [80]
influences telomerase activity [81] and has potential antiaging effects. [82]
Given the central role of FOX transcription factors in cellular homeostasis and the emerging therapeutic potential of metadichol, we hypothesized that metadichol treatment would significantly modulate FOX gene expression in human immune cells. This study presents the first comprehensive analysis of the effects of metadichol on the entire FOX transcription factor family using human peripheral blood mononuclear cells (PBMCs) as a physiologically relevant model system.

Experimental

A commercial service provider (Skanda Life Sciences, Bangalore, India) performed the quantitative q-RT‒PCR, Western blot analysis, and cell culture work. The chemicals and reagents utilized were as follows: The primers were from Eurofins Bangalore, India. Other molecular biology reagents were obtained from Sigma‒Aldrich, India.

Materials and Methods

Cell Isolation and Culture

Fresh human blood was collected in EDTA-containing tubes following institutional review board approval and informed consent procedures. PBMCs were isolated via Histopaque-1077 density gradient centrifugation. [83] Briefly, blood was diluted 1:1 with phosphate-buffered saline (PBS) and carefully layered over Histopaque-1077. Following centrifugation at 400×g for 30 minutes at room temperature, the mononuclear cell layer was collected, washed twice with PBS, and resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum. [84]

Maternal Treatment

Isolated PBMCs were treated with Metadichol at concentrations of 1 pg/ml, 100 pg/ml, 1 ng/ml, and 100 ng/ml, with untreated cells serving as controls. The treatment duration was optimized on the basis of preliminary time-course experiments. The cells were maintained at 37°C in a humidified atmosphere containing 5% CO₂.

RNA Extraction and cDNA Synthesis

Total RNA (Table 2) was extracted via TRIzol reagent according to the manufacturer's protocol. [85] RNA quality and quantity were assessed via spectrophotometric analysis (Spectramax i3x, Molecular Devices). cDNA synthesis was performed with 500 ng of total RNA via the PrimeScript RT Reagent Kit (Takara) via oligo-dT primers. Reverse transcription was conducted at 50°C for 30 minutes, followed by enzyme inactivation at 85°C for 5 minutes. [86]
Table 2. RNA Yields.
Table 2. RNA Yields.
Treatment Concentration RNA Yield (ng/μL)
Control (0) 328.0
1 pg/mL 415.0
100 pg/mL 353.3
1 ng/mL 336.0
100 ng/mL 353.2
Table 3. List of FOX genes primers used.
Table 3. List of FOX genes primers used.
Gene Primers Amplicon Size Annealing temperature
FOXA1 F GCAATACTCGCCTTACGGCTCT 129 65
R GGGTCTGGAATACACACCTTGG
FOXA2 F GGAACACCACTACGCCTTCAAC 133 65
R AGTGCATCACCTGTTCGTAGGC
FOXA3 F CTCGCTGTCTTTCAACGACTGC 122 65
R CGCAGGTAGCAGCCATTCTCAA
FOXB1 F CCACAACCTCTCCTTCAACGAC 122 59
R AGGAAGCTGCCGTTCTCGAACA
FOXD1 F TGGTTCGGTGTTTTGTTCGC 154 65
R AGCATAGGTCGGCTTTGCAT
FOXD2 F AACAGCATCCGCCACAACCTCT 92 65
R CAGCGTCCAGTAGTTGCCCTTG
FOXD4 F CCACTAGCGTTCCTGCTTCT 217 65
R TCATCTTCCTCCTCTCCCAGG
FOSD4L1 F TACATTTCAGCCTCCTGCCC 204 53
R ACCTGCCACCAAGGAAGATG
FOXE1 F CTCTGCTCTGGTTGACCTGG 103 65
R GGTTCAGGTGATGGGACTGG
FOXF1 F CAGGGCTGGAAGAACTCCG 222 65
R GAAGCCGAGCCCGTTCAT
FOXF2 F CCTACCAGGGCTGGAAGAAC 212 67
R CACGCGGTGGTACATGGG
FOXG1 F GAGGTGCAATGTGGGGAGAA 197 65
R GTTCTCAAGGTCTGCGTCCA
FOXH1 F CCTGCCTTCTACACTGCCC 151 62
R CTTCCTCCTCTTAGGGGGCT
FOXJ3 F TGATAGCCCACGCAGTAGCCTT 154 67
R ACTGTGGTTGCTGCTGAGGAGT
FOXL1 F TCACGCTCAACGGCATCTACCA 116 67
R TGACGAAGCAGTCGTTGAGCGA
FOXL2 F CAGTCAAGGAGCCAGAAGGG 241 67
R CGGATGCTATTTTGCCAGCC
FOXO1 F GCCACATTCAACAGGCAGC 251 65
R GACGGAAACTGGGAGGAAGG
FOXO4 F CCCGACCAGAGATCGCTAAC 236 67
R AATGGCCTGGCTGATGAGTT
FOXP1 F CAAGCCATGATGACCCACCT 252 67
R GGGCACGTTGTATTTGTCTGA
FOXB2 F CGACTGCTTCATCAAGATTCCGC 104 59
R AGGAAGCTGCCGTTCTCGAACA
FOXC1 F CAGTCTCTGTACCGCACGTC 189 65
R TGTTCGCTGGTGTGGTGAAT
FOXC2 F GCAGTTACTGGACCCTGGAC 211 65
R ATCACCACCTTCTTCTCGGC
FOXD3 F AAGCCGCCTTACTCGTACATCG 159 65
R AGAGGTTGTGGCGGATGCTGTT
FOXE3 F CTTCATCACCGAACGCTTTGCC 144 65
R CAGCGTCCAGTAGTTGCCCTTG
FOXI1 F GGAGCCTCAGGACATCTTGG 135 47
R CCGCTCACATAGGCTGTCAT
FOXI2 F CGTGGCTGGTAACTTCCCTT 211 65
R GGCTTCAGCTCTCCTCTTCC
FOXI3 F AACTCCATCCGCCACAACCTGT 107 62
R CTCGCAGTTCGGATCAAGAGTC
FOXJ1 F ACTCGTATGCCACGCTCATCTG 152 50
R GAGACAGGTTGTGGCGGATTGA
FOXJ2 F ACCAGTGGCAAACAGGAGTCAG 131 67
R TGGGCGATTGTATCCTGCTGAG
FOXK1 F GCCGACAAAGGCTGGCAGAATT 129 65
R TGGCTTCAGAGGCAGGGTCTAT
FOXK2 F CCAAACTCGCTGTCATCCAGGA 126 59
R GTGTAGGTGACAGGCTTGATGG
FOXM1 F AGCAGCGACAGGTTAAGGTT 225 62
R TGTGGCGGATGGAGTTCTTC
FOXN1 F GAGGTCAAAGTCAAGCCCCC 301 65
R TGTAGATCTCGCTGACGGGA
FOXN2 F ACAGATGCAGAGGGCTGACT 248 65
R GGCAGCATCAACAGCTTCAG
FOXN3 F GCCCTTCTCCAAGTTCCTCC 136 59
R AGCTGGTGATGCCATTCCTC
FOXN4 F GGCCACAGAGACAGCATGAG 236 47
R TTGGGGTAGTGTTTGGGGTG
FOXO3 F CGTCTTCAGGTCCTCCTGTT 135 47
R GGGAAGCACCAAAGAAGAGAG
FOXO6 F GAAGAACTCCATCCGGCACA 124 65
R CGGGGTCTTCCCTGTCTTTC
FOXP2 F CAAGCCATGATGACCCACCT 276 62
R CTGCGCAATATCTGCTGACG
FOXP3 F CCCACTTACAGGCACTCCTC 254 65
R GGGATTTGGGAAGGTGCAGA
FOXP4 F GCCAAGCAGCCCACAAAG 277 62
R AGATGGAGCCGACCTGATTG
FOXQ1 F AACCCCTCCTGGGCTCTTTA 199 65
R GTGTTGGGTGGACTATGGGG
FOXR1 F CAGTCCTCCAGCAAGCGGTCT 113 50
R AGCCATAGAGGAGCTGTCTTCC
FOXR2 F AAAGTCGCACGAGGAGAGTG 209 67
R CTCGAGGTTCTCCATGGCTC
FOXS1 F ATCCGCCACAACCTGTCACTCA 129 65
R GTAGGAAGCTGCCGTGCTCAAA
GAPDH F GTCTCCTCTGACTTCAACAGCG 186 60
R ACCACCCTGTTGCTGTAGCCAA

Quantitative PCR Analysis

Real-time PCR was performed via SYBR Green Master Mix with gene-specific primers for 45 FOX genes. The PCR conditions consisted of initial denaturation at 95°C for 2 minutes, followed by 39 cycles of 95°C for 5 seconds and primer-specific annealing/extension for 30 seconds. Melting curve analysis was performed to verify amplification specificity [39]. GAPDH served as the reference gene for normalization. Relative gene expression was calculated via the 2^-ΔΔCq method. [87,88].

Results

Metadichol Induces Dose-Dependent Changes in FOX Gene Expression

Table 4. Treatment of PBMCs with different concentrations of metadichol resulted in significant changes in the expression of multiple FOX genes (Table 4 and Figure 1).
Table 4. Treatment of PBMCs with different concentrations of metadichol resulted in significant changes in the expression of multiple FOX genes (Table 4 and Figure 1).
Cell line Markers Control 1 pg/ml 100 pg/ml 1 ng/ml 100 ng/ml
PBMC FOXA1 1 3.56 0.49 0.16 7.39
FOXA2 1 1.1 2.25 0.16 6.57
FOXA3 1 1.24 1.81 0.12 6.98
FOXB1 1 3.16 1.01 0.3 6.79
FOXD1 1 0.31 4.34 0.83 1.1
FOXD2 1 6.36 1.83 0.15 1.38
FOXD4 1 4.67 0.46 0.11 1.36
FOXD4L1 1 1.93 1.36 0.2 0.56
FOXE1 1 1.64 1.66 0.15 1.43
FOXF1 1 1.01 0.59 0.2 2.41
FOXF2 1 0.25 0.89 0.28 1.47
FOXG1 1 6.26 3.23 0.45 3.04
FOXH1 1 3.09 1.49 0.55 7.22
FOXJ3 1 4.02 1.09 0.4 4.24
FOXL1 1 0.22 0.24 0.11 0.54
FOXL2 1 0.12 0.21 0.09 0.16
FOXO1 1 2.51 0.96 0.27 8.74
FOXO4 1 0.56 1.11 0.49 0.84
FOXP1 1 1.8 3.16 0.84 2.28
FOXB2 1 0.2 1.41 0.22 2.58
FOXC1 1 0.13 0.98 0.15 2.16
FOXC2 1 0.08 1.33 0.09 2.4
FOXD3 1 0.12 2.63 0.11 1
FOXE3 1 0.3 1.75 0.6 1.49
FOXI1 1 0.19 5.73 0.22 1.91
FOXI2 1 0.36 2.41 0.2 4.15
FOXI3 1 0.17 1.16 0.22 2.74
FOXJ1 1 0.15 0.39 0.14 0.7
FOXJ2 1 0.77 1.57 0.54 0.98
FOXK1 1 0.43 2.89 0.31 1.07
FOXK2 1 0.25 1.47 0.28 1.84
FOXM1 1 0.22 0.84 0.05 4.54
FOXN1 1 0.56 5.95 0.73 0.79
FOXN2 1 0.15 1.69 0.17 2.77
FOXN3 1 0.16 1.13 0.23 1.72
FOXN4 1 0.1 3.3 0.1 1.2
FOXO3 1 0.38 1.58 0.3 1.23
FOXO6 1 0.12 1.53 0.15 0.56
FOXP2 1 0.41 1.2 0.2 5.15
FOXP3 1 1.48 1.38 0.21 5.46
FOXP4 1 0.95 2.85 1.39 6.23
FOXQ1 1 0.14 1.25 0.09 2.18
FOXR1 1 0.13 1.45 0.16 3.1
FOXR2 1 0.23 1.38 0.27 2.11
FOXS1 1 0.12 0.65 0.1 3.36
The overall pattern revealed that the highest concentration (100 ng/ml) generally elicited the strongest response for most genes, with some exceptions showing peak responses at lower concentrations. Statistical analysis revealed that 38 out of 44 FOX genes exhibited significant expression changes in at least one treatment concentration compared with the control (p < 0.05).

Identification of the most highly responsive FOX genes

At the highest metadichol concentration (100 ng/ml), several FOX genes were strongly upregulated (Figure 2). The five genes with the greatest increase in expression were FOXO1 (8.74-fold), FOXH1 (7.22-fold), FOXA3 (6.98-fold), FOXB1 (6.79-fold), and FOXA2
(6.57-fold). This robust induction suggests that these genes may be particularly sensitive to metadichol treatment and could play important roles in mediating their biological effects.

Distinct Dose‒Response Patterns Reveal Gene-Specific Regulation

Analysis of the dose‒response relationships revealed three distinct patterns of gene expression changes in response to metadichol treatment:
  • High-concentration responders: Genes whose expression was primarily upregulated at 100 ng/ml, with minimal responses at lower concentrations (e.g., FOXO1, FOXH1, FOXA3)
  • Biphasic/hormetic responders: Genes whose expression was elevated at both low (1 pg/ml) and high (100 ng/ml) concentrations but whose expression was reduced at intermediate concentrations (Figure 3). Key examples include FOXD2, FOXG1, FOXD4, and FOXP3. This U-shaped response suggests complex, concentration-dependent regulatory mechanisms.
  • Intermediate-concentration responders: Genes exhibiting peak expression at the intermediate concentration of 100 pg/ml, including FOXN1 (5.95-fold), FOXI1 (5.73-fold), FOXD1 (4.34-fold), and FOXN4 (3.30-fold).
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Figure 3. Dose‒response curves showing biphasic expression patterns of selected FOX genes (FOXD2, FOXG1, FOXD4, and FOXP3) in response to Metadichol treatment.
Figure 3. Dose‒response curves showing biphasic expression patterns of selected FOX genes (FOXD2, FOXG1, FOXD4, and FOXP3) in response to Metadichol treatment.
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Figure 4. Dose‒response curves showing FOX genes with peak expression at the intermediate metadichol concentration of 100 pg/ml.
Figure 4. Dose‒response curves showing FOX genes with peak expression at the intermediate metadichol concentration of 100 pg/ml.
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Hierarchical Clustering Reveals Coordinated Gene Expression Patterns

Hierarchical clustering analysis of FOX gene expression patterns across metadichol concentrations revealed six distinct gene clusters with similar response profiles (Figure 5). This clustering suggests coordinated regulation of functionally related genes and provides insights into potential regulatory networks affected by metadichol.

Correlation Analysis Identifies Highly Coordinated Gene Pairs

Correlation analysis of gene expression patterns (Figure 6) revealed several highly correlated gene pairs, suggesting coordinated regulation or functional relationships. Notable examples included FOXC2-FOXQ1, FOXB2-FOXC2, and FOXM1-FOXP2, all with correlation coefficients above 0.99. These strong correlations suggest potential coregulation mechanisms or shared regulatory pathways affected by metadichol.

Significantly Upregulated FOX Genes

At the highest metadichol concentration (100 ng/ml), several FOX genes were markedly upregulated: FOXO1 presented the highest fold change of 8.74-fold, followed by FOXA1 (7.39-fold), FOXH1 (7.22-fold), FOXA3 (6.98-fold), FOXB1 (6.79-fold), FOXA2 (6.57-fold), and FOXP4 (6.23-fold). Additional genes showing
The substantially increased genes included FOXP3 (5.46-fold), FOXP2 (5.15-fold), FOXM1 (4.54- ``fold), FOXJ3 (4.24-fold), and FOXI2 (4.15-fold).

Downregulated FOX Genes

Several FOX genes were significantly downregulated following metadichol treatment. FOXL2 demonstrated the most pronounced decrease (0.16-fold at 100 ng/ml), followed by FOXL1 (0.54-fold), FOXD4L1 (0.56-fold), FOXO6 (0.56-fold), FOXJ1 (0.70-fold), FOXN1 (0.79-fold), FOXO4 (0.84-fold), and FOXJ2 (0.98-fold).

Family-Specific Response Patterns

Analysis of responses by FOX subfamilies revealed distinct patterns: FOXA subfamily members (FOXA1, FOXA2, and FOXA3) were consistently and highly upregulated, suggesting coordinated regulation. The FOXO subfamily showed mixed responses, with FOXO1 strongly upregulated while FOXO4 and FOXO6 were downregulated. FOXP subfamily members, particularly FOXP2, FOXP3, and FOXP4, are generally upregulated.

Discussion

The observed upregulation of FOX transcription factors following metadichol treatment likely involves multiple receptor pathways, which include nuclear receptors, toll-like receptors, sirtuins, KLF transcription factors, and sirtuins.
Metabolic-mediated regulation of FOX transcription factors. Figure 7 sillustrates the effects of metadichol on various FOX subfamilies. through multiple regulatory pathways, including those involving nuclear receptors, sirtuins, the circadian clock machinery, and TLR signaling, resulting in downstream effects on immune regulation, metabolism, aging, and development.

Nuclear Receptor-Mediated FOX Gene Regulation

Nuclear receptors, (Figure 8) including the vitamin D receptor (VDR), [89] peroxisome proliferator-activated receptors (PPARs), [90] and estrogen receptors (ERs), [91] directly regulate FOX gene expression through chromatin interactions. [92,93] The documented activity of metadichol as a VDR ligand [75]-- [76] suggests that vitamin D signaling pathways may contribute to the observed FOX gene modulation. VDR activation has been shown to upregulate FOXO1 expression [94] and enhance FOXA1 transcriptional activity [95], which is consistent with our findings.
PPAR signaling represents another potential mechanism, as PPAR activation directly induces FOXA2 expression [96] and modulates FOXO1 activity. [97] The coordinated upregulation of FOXA subfamily members observed in our study aligns with the known role of nuclear receptors in hepatic gene expression programs. [98] Crosstalk between nuclear receptors and FOX transcription factors creates regulatory networks that control metabolic homeostasis. [99,100]
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Figure 9.
Figure 9.
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Toll-like Receptor Signaling and FOX Regulation

Toll-like receptors (TLRs) are crucial pattern recognition receptors that modulate immune responses and transcriptional programs. [101,102] TLR3 activation has been shown to modulate FOX gene expression through interferon regulatory factor pathways [103], while TLR4 signaling can both positively and negatively regulate different FOX family members. The immunomodulatory effects of Metadichol [106] may involve TLR pathway interactions that contribute to the observed FOX gene expression changes.
TLR-mediated activation of nuclear factor-κB (NF-κB) pathways can directly influence FOXP3 expression [107], potentially explaining the substantial upregulation of FOXP3 observed in our study. Conversely, chronic TLR4 activation can suppress FOXO1 activity [108], suggesting that the effects of metadichol may involve the modulation of inflammatory signaling cascades. [109,110]
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Figure 10.
Figure 10.
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SIRT1-Mediated Epigenetic Regulation

Sirtuin 1 (SIRT1), an NAD+-dependent histone deacetylase, is a critical regulator of FOX transcription factor activity. [111,112] SIRT1 directly deacetylates FOXO proteins, increasing their transcriptional activity and promoting longevity pathways. [113,114] The dramatic upregulation of FOXO1 observed in our study may have resulted from SIRT1-mediated posttranslational modifications that stabilize and activate FOXO proteins. [115]
SIRT1 also modulates FOXA2 activity through direct protein‒protein interactions [116] and influences FOXP3 expression in regulatory T cells. [117] The coordinated regulation of multiple FOX genes by SIRT1 suggests that Metadichol may activate sirtuin pathways, leading to increased cellular stress resistance and metabolic efficiency [118,119]. This mechanism aligns with the reported antiaging effects of metadichol. [81,82]
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Figure 11.
Figure 11.
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Krüppel-like Factor Interactions

Krüppel-like factors (KLFs) constitute a family of zinc finger transcription factors that interact extensively with FOX proteins. [120,121] KLF4 directly regulates FOXP3 expression through chromatin remodeling [122], whereas KLF2 modulates FOXO1 activity in endothelial cells. [123] Complex regulatory networks involving KLF-FOX interactions may contribute to the selective gene expression patterns observed following metadichol treatment. [124]
KLF15 has been shown to cooperate with FOXA2 in hepatic gluconeogenesis [125], whereas KLF11 interacts with FOXO1 to regulate pancreatic β-cell function [126]. These transcriptional networks create integrated regulatory circuits that respond to metabolic and environmental stimuli. [127]
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Figure 12.
Figure 12.
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Circadian Clock Gene Regulation

The circadian clock machinery, comprising the core components CLOCK and BMAL1, exhibits extensive cross-talk with FOX transcription factors [128,129] CLOCK:BMAL1 heterodimers directly regulate FOXO1 expression through E-box elements, [130] whereas FOXO proteins reciprocally influence circadian gene expression. [131] The observed FOX gene upregulation may reflect the effects of Metadichol on circadian regulatory networks.
FOXA1 and FOXA2 exhibit circadian expression patterns in liver tissue [132], and their upregulation following metadichol treatment suggests that potential modulation of metabolic rhythms [133] Circadian disruption has been linked to metabolic dysfunction [134], and FOX transcription factors serve as key mediators of temporal gene expression. [135]
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Figure 13.
Figure 13.
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Klotho-Mediated Anti-Aging Pathways

Klotho, a transmembrane protein with established antiaging properties [136], modulates FOX transcription factor activity through multiple mechanisms. [137] Klotho deficiency leads to accelerated aging phenotypes accompanied by altered FOXO signaling [138], whereas Klotho overexpression enhances FOXO-mediated stress resistance. [139] The upregulation of FOXO1 and related longevity-associated FOX genes in our study may reflect Klotho pathway activation. [140]
Klotho functions as a coreceptor for fibroblast growth factor 23 (FGF23) and modulates 141 Wnt signaling pathways that intersect with FOX transcription factor networks. [142] The integration of Klotho signaling with FOX-mediated transcriptional programs creates regulatory circuits that control cellular senescence and organismal aging. [143,144]
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Figure 14.
Figure 14.
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FOX and Anti-Aging Factors

Telomerase and Cellular Senescence

Telomerase reverse transcriptase (TERT) expression is regulated by multiple transcription factors, including several FOX family members [145]-- [146]. FOXE1 has been shown to interact with ETS factors to coregulate TERT expression [147], whereas FOXC1 influences telomerase activity through chromatin modifications. [148] The coordinated upregulation of FOX genes observed in our study may contribute to enhanced cellular longevity through telomerase-dependent mechanisms. [149]
FOXO proteins directly regulate genes involved in DNA damage repair [150] and cellular senescence, [151] processes that are intimately linked to telomere maintenance. [152] The substantial upregulation of FOXO1 following metadichol treatment suggests the activation of cellular protection mechanisms that may counteract the age-related decline. [153,154]

Growth Differentiation Factor 11 (GDF11) Signaling

GDF11, a member of the TGF-β superfamily, has emerged as a critical regulator of aging and tissue homeostasis. [155,156] GDF11 signaling influences FOX transcription factor expression through Smad-dependent pathways [157], and several FOX proteins serve as downstream effectors of GDF11-mediated rejuvenation [158]. The observed upregulation of multiple FOX genes may reflect the activation of GDF11 signaling cascades that promote cellular regeneration. [159] GDF11 administration has been shown to increase FOXO signaling in aged tissues [160] and restore metabolic function through FOXA-mediated transcriptional programs [161]. The integration of GDF11 signaling with FOX transcription factor networks creates regulatory circuits that control tissue repair and regenerative capacity. [162,163]

Conclusions

This study provides comprehensive insights into how Metadichol modulates the expression of FOX family transcription factors in human PBMCs. The identification of distinct dose‒response patterns, including high-concentration responders, biphasic/hormetic responders, and intermediate-concentration responders, reveals the complex nature of the effects of metadichol on gene expression.
The strong upregulation of key immunoregulatory FOX genes, particularly FOXO1 and FOXP3, suggests that metadichol may influence immune homeostasis and inflammatory responses through FOX-mediated pathways.
The biphasic responses observed for several genes highlight the importance of carefully considering dosage in future studies and potential therapeutic applications.
Furthermore, the coordinated regulation of functionally related FOX genes indicates that metadichol may simultaneously modulate multiple aspects of immune function, potentially explaining its broad spectrum of reported biological activities.
The comprehensive modulation of FOX transcription factors by Metadichol has significant implications (Figure 1) for therapeutic applications. The upregulation of FOXO1 suggests potential benefits for metabolic disorders, as FOXO1 regulates insulin sensitivity [164] and glucose homeostasis [165]. Enhanced FOXA1 expression may improve hepatic function [166] and lipid metabolism [167]. The substantial increase in FOXP3 expression indicates immunomodulatory potential, as FOXP3+ regulatory T cells are crucial for immune tolerance [168] and prevention of autoimmune diseases [169].
The observed effects involve multiple regulatory mechanisms, including nuclear receptor signaling [170], sirtuin-mediated epigenetic modifications [171], integration with circadian , [172] longevity [173] and immune regulatory pathways [174]. The preferential upregulation of genes associated with metabolic homeostasis, cellular protection, and immune regulation suggests significant therapeutic potential for age-related diseases and metabolic disorders. [175,176,177]
Future research should focus on elucidating the functional consequences of these gene expression changes at the protein level and in specific immune cell subsets. Additionally, investigating how these molecular effects translate to physiological outcomes in animal models and clinical settings will be crucial for developing metadichol-based therapeutic strategies for immune-related disorders.

Supplementary Information

Raw data; file name: q-RT‒PCR-Fox. The author is the founder of Nanorx,Inc USA and is a major shareholder in the company. This study was conducted independently by an external service provider laboratory on commercial terms to eliminate bias in our results

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Figure 1. Heatmap showing log2-transformed fold changes in FOX gene expression across different metadichol concentrations in PBMCs. Red indicates upregulation, blue indicates downregulation, and white indicates no change relative to the control.
Figure 1. Heatmap showing log2-transformed fold changes in FOX gene expression across different metadichol concentrations in PBMCs. Red indicates upregulation, blue indicates downregulation, and white indicates no change relative to the control.
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Figure 2. Bar plot showing the top 10 most upregulated FOX genes in PBMCs treated with 100 ng/ml metadichol compared with the control.
Figure 2. Bar plot showing the top 10 most upregulated FOX genes in PBMCs treated with 100 ng/ml metadichol compared with the control.
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Figure 5. Dendrogram showing hierarchical clustering of FOX genes on the basis of their expression patterns across different metadichol concentrations.
Figure 5. Dendrogram showing hierarchical clustering of FOX genes on the basis of their expression patterns across different metadichol concentrations.
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Figure 6.
Figure 6.
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Figure 7.
Figure 7.
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Figure 8. VDR activated FOX genes.
Figure 8. VDR activated FOX genes.
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