Title: Metadichol® is a nano lipid emulsion that expresses all 48 nuclear receptors in stem and somatic cells

Abstract

Human nuclear receptors (NRs) involve 49 ligand-dependent transcription factors that are important for regulating the cell cycle and processes. There are many literature references to work on NR expression in many organs, abnormal cells, and tissues. However, a simple universal method to study the expression of NR is still missing. Here, we present systematic profiling of NRs in human umbilical cord stem cell lines and assess the expression of the 48 human NRs by quantitative real-time (qRT)-PCR using Metadichol, a nanoemulsion made of natural lipid alcohol. Metadichol-treated umbilical cord cells and fibroblasts, where all cells expressed NRs at a concentration range of 1 pg-100 ng/mL in a dependent manner, were detected by qRT-PCR and qualified by Western blotting. This method will allow the study of many organs and tissues and expand our understanding of the role of NRs and their role in mitigating diseases.

Full text

Title: Metadichol® is a nano lipid emulsion that expresses all 48 nuclear receptors in stem and somatic cells
Palayakotai R. Raghavan
Nanorx Inc
PO Box 1131
Chappaqua, NY 10514, USA
Tel; 914-671-0224
Correspondence:
raghavan@nanorxinc.com
Summary
Human nuclear receptors (NRs) involve 49 ligand-dependent transcription factors that are important for
regulating the cell cycle and processes. There are many literature references to work on NR expression in many
organs, abnormal cells, and tissues. However, a simple universal method to study the expression of NR is still
missing. Here, we present systematic profiling of NRs in human umbilical cord stem cell lines and assess the
expression of the 48 human NRs by quantitative real-time (qRT)-PCR using Metadichol, a nanoemulsion made
of natural lipid alcohol. Metadichol-treated umbilical cord cells and fibroblasts, where all cells expressed NRs
at a concentration range of 1 pg-100 ng/mL in a dependent manner, were detected by qRT-PCR and qualified
by Western blotting. This method will allow the study of many organs and tissues and expand our
understanding of the role of NRs and their role in mitigating diseases.
Highlights
Metadichol treatment of somatic cells leads to nuclear receptor expression
Depending on the cell and concentration of Metadichol used, fold changes are different
The method could be used to study many cells and disease cells to better understand NR expression patterns and
implications.
The results suggest that Metadichol is a universal ligand to all nuclear receptors.
Keywords: Nuclear receptors, stem cells, fibroblasts, AHR, vitamin C, Sertolli cells, chromatin, qRT-PCR,
Metadichol, nanoemulsion
!

Introduction
Nuclear receptors (NRs) are transcription factors usually activated by small lipophilic molecules. (McEwan and
Kumar, 2015). The 49 AHR (aryl hydrocarbon receptor) NRs, which are highly conserved in the human
genome, are subdivided into seven subfamilies based on amino acid sequences (NOR, NR1, NR2, NR3, NR4,
NR5, and NR6) (Frigo et al., 2021). Many NRs are classified as orphan receptors because they lack a specific
ligand for activation (
Sladek, 2011
). Unliganded NRs are bound to heat shock protein 90 (HSP90) and can be
found in the cytosol and nucleus. Receptors may also bind to corepressors without ligands (Li et al., 2013).
1,25-dihydroxyvitamin D3 is a small molecule that can diffuse through the cell membrane and bind to NRs
located in the cytosol or nucleus of the cell (Pike and Meyer, 2010). This binding leads to several downstream
events that eventually result in up- or downregulation of gene expression. The NR ligands exhibit a broad
spectrum of full, partial, inverse agonist, or antagonist activities. Selective NR modulators activate only a subset
of the functions induced by the ligand or act in a cell-type-selective manner (Burris et al., 2013).
Chromatin plays a crucial role in the actions of NR by modulating interactions with regulatory elements in
the genome. However, when receptor binding occurs, chromatin changes that impact receptor signaling (Biddie
and John, 2014). NRs bind to sDNA sequences, leading to modification of the chromatin structure. The final
outcome leads to modulation of RNA polymerase activity resulting in increased or decreased transcription.
transcription (Zaret and Yamamoto, 1984). Chromatin modification leads to pathways that impact NR action in
various cells. Chromatin remodeling leads to numerous processes that involve pluripotency, cellular
differentiation, inflammation, DNA damage and repair and tumor suppression (Xie et al., 2009).
NR regulation and its tissue-expression profile along with the associated cofactors are necessary to separate
desirable therapeutic efficacies from undesirable side effects. One of the least studied areas is the regulation of
NR ligands by NRs themselves.
Chang-Qing Xie et al. reported NR expression profiles in human and mouse embryonic stem cell lines and
during their early differentiation into embryoid bodies (Klemm et al., 2019). The expression of the 48 human
and mouse NRs was assessed by quantitative real-time (qRT)-PCR. Hong et al. evaluated the expression of the
receptors for estrogen, progesterone, and glucocorticoids (2004). Very little is known about systemic NR
expression in human cell lines. In this research, we present the specific roles of NRs in human cell lines by
characterizing the RNA and cDNA expression profiles of the NR superfamily while treating stem cells and
fibroblasts with metadichol, which is a nanoemulsion of long-chain alcohols (Raghavan, 2015), using qRT-PCR
and Western blotting methods.
Methods
The methods, including qRT-PCR, Western blotting, and cell culture, were carried out by a commercial service
provider—Skanda Life sciences, Bangalore, India.
The procurement of chemicals and reagents was as follows. Human mesenchymal stem cells and normal
human dermal fibroblast cells were procured from ATCC (USA). Primary antibodies were from either
ABclonal, Woburn, (both in Massachusetts, USA) or Elabscience, Maryland, USA. The primers were from
Sahagene, Hyderabad, India. Other molecular biology reagents were from Sigma-Aldrich, India.

Cell maintenance and seeding
We preserved the cells in a suitable medium with 1% antibiotics in a wet atmosphere of 5% CO2 at 37 °C. We
changed the medium every two days until the cells reached confluency, and we assessed their viability using a
hemocytometer.
At 70%-80% confluency, we prepared and seeded single-cell suspensions containing 106 cells/mL in six-
well plates at a density of 106 cells/well. We incubated the cells for 24 h at 37 °C in 5% CO2, rinsed the cell
monolayer with serum-free medium and treated it with Metadichol at preset concentrations.
Cell treatment
We prepared Metadichol in serum-free media at different concentrations (1 pg/mL, 100 pg/mL, 1 ng/mL, and
100 ng/mL) and then added them to predesignated wells. Note that the control cells were drug-free. We
incubated the cells for 24 h. Next, we washed them gently with sterile PBS. We isolated RNA using TRIzol
following the manufacturer"s instructions and prepared cDNA followed by the analysis of several biomarkers
using qPCR and Western blotting.
RNA isolation
We separated total RNA using TRIzol reagent (Invitrogen) and collected approximately 106 cells in 1.5 mL
microcentrifuge tubes. The cells were centrifuged at 5,000 rpm for 5 min at 4 °C, and the supernatant was
removed. Next, we added 650 µL of TRIzol to the pellet and mixed the solution to be incubated on ice for 20
min. Subsequently, we added 300 µL of chloroform and mixed the samples well by gentle inversion for 1–2
min and then incubated them on ice for 10 min. We centrifuged the samples at 12,000 rpm for 15 min at 4 °C
and carefully transferred the upper aqueous layer to a new sterile 1.5 mL centrifuge tube, to which we added an
equal amount of prechilled isopropanol and incubated the samples at −20 °C for 60 min. Afterward, we
centrifuged the mixture at 12,000 rpm for 15 min at 4 °C and prudently removed the supernatant. The RNA
pellets were retained and washed with 1.0 mL of 100% ethanol, followed by 700 µL of 70% ethanol via
centrifugation as described previously after each step. We air-dried the RNA pellets at room temperature for
almost 15–20 min and then resuspended them in 30 µL of DEPC-treated water. We quantified the RNA
concentration using a SpectraMax i3x - SpectraDrop Micro-Volume Microplate (Molecular Devices, USA) and
synthesized cDNA using reverse transcription-PCR.
cDNA synthesis
Following the manufacturer"s guidelines, we synthesized cDNA from 2 µg of RNA using the PrimeScript
cDNA synthesis kit (Takara, France) and oligo dT primers. The reaction volume was 20 μL, and cDNA
synthesis was performed at 50 °C for 30 min, followed by 85 °C for 5 min on an Applied Biosystems
instrument (Veritii). The obtained cDNA was used in the following step for qPCR.
Primers and qPCR
The PCR blend (final volume of 20 µL) contained 1 µL of cDNA, 10 µL of SYBR green Master Mix, and 1 µM
complementary primers specific for the particular target genes. We ran the samples at the following settings:
primary denaturation at 95 °C for 5 min followed by 30 cycles of a secondary at 95 °C for 30 s, then hardening
at the optimized temperature for 30 s, with an extension at 72 °C for 1 min. We identified the number of cycles

that allowed amplification in the exponential range without reaching a plateau as the optimal number of cycles.
We evaluated the results using CFX Maestro software and computed the fold change via the following equation.
ΔΔCT method
We determined the relative expression of the target gene in relation to the housekeeping gene (β-actin) and
untreated control cells by the comparative CT method.
The ΔCT for each treatment was calculated using the formula:
ΔCT = CT (target gene) – CT (reference genes).
To obtain a ΔΔCT, we subtracted the individual samples between the treated and control groups as follows:
ΔΔCT = ΔCT (treatment group) – ΔCT (control group).
Similarly, we calculated the fold change in the target gene expression for each treatment using the formula.
Fold change = 2^ (-ΔΔCT)
Protein isolation
We isolated total protein from 106 cells using RIPA buffer supplemented with the protease inhibitor PMSF. We
applied a mild inversion for 30 min at 4 °C to lyse the cells and then centrifuged them at 10,000 rpm for 15 min.
Finally, we transferred the supernatant to a fresh tube and determined the protein concentration using the Bradford
method, where 25 µg of protein was mixed with 1X sample loading dye containing SDS and loaded on a gel.
Under denaturing conditions, we separated the proteins using Tris-glycine running buffer.
Western blotting
We transferred the proteins to methanol-activated PVDF membranes (Invitrogen, USA) using a Turbo transblot
system (Bio Rad, USA). We blocked the membranes with 5% BSA for 1 h and incubated them with the respective
primary antibody overnight at 4 °C followed by a species-specific secondary antibody for 1 h at room temperature.
We rinsed the blots and incubated them with ECL substrate (Merck, USA) for 1 min in the dark and captured the
images at suitable exposure settings using a ChemiDoc XRS system (Bio Rad, USA).
Table 1 List of primers
Gene
Primers
Base pairs
NR1C2/PPARD
F
CCTTCTCAAGTATGGCGTGC
226
R
GATGGCCGCAATGAATAGGG
RXRG
F
CAGGAAAGCACTACGGGGTA
254
R
CCTCACTCTCAGCTCGCTCT
PPARG
F
AGAAGCCTGCATTTCTGCAT
236
R
TCAAAGGAGTGGGAGTGGTC
NR2F1
F
CATTTTTGGGCGATCTCCAGG
261
R
GCCTTCTTCTTTCGGGAGGT
HNF4A
F
ACTGCCACGTACCTGTGCCT
274

R
AGGCATGCGAGTTGTGACCA
HNF4G
F
AGCTGGCATATCTCAGCTGGC
185
R
AACACCTGGCTGGCAATCGG
NR2F2
F
CTCAACTGCCACTCGTACCT
253
R
TCAACACAAACAGCTCGCTC
NR1I3
F
CAGCAAACACCTGTGCAACT
189
R
TGCGAAGTGTGTGACCAGAG
NR1H4
F
AACAGAACAAGTGGCAGGTC
201
R
AGAGTCTCAGCTGGCATACG
ESR1
F
GATGTGGGAGAGGATGAGGA
165
R
TCAGGCATGCGAGTAACAAG
ESR2
F
TTCAGCCTGTGACCTCTGTG
178
R
CTTGGTTTGTCCAGGACGTT
ESRRA
F
CAGGGGAGCATCGAGTACAG
303
R
CTTCTCAGGCTCAACCACCA
NR1D2
F
AGTTCTTCCAGCTCAGCCTC
226
R
TTGTCATCCCAGGTGCACTC
ESRRB
F
CTTGGTTTGTCCAGGACGTT
264
R
TTTTCCATCATGGCTTGACA
NR5A2
F
TCCAGCTTCCAGGCAGCCTC
234
R
GATCTGTGAATCTGCGTT
NR3C2
F
CTGCCTCGTTTCCCTTTTCC
231
R
CCATGATCTGTGCGTTCCTG
NR0B2
F
GCTGTCTGGAGTCCTTCTGG
164
R
CTGGGTATGAATCCCAGCAC
ESRRG
F
GACTTGACTCGCCACCTCTC
174
R
GTGGTACCCAGAAGCGATGT
NR4A2
F
CCGGTGTCTAGTTGCCAGAT
275
R
ACGCCGTAGTGTTGGCAG
NR2F6
F
GGACTCTGGCTTCTCTCCTC
187
R
TAGGGGTGCTGAGGAACAAG

NR6A1
F
GAGGAACAGGTGCCAGTACT
175
R
GGCCTCTTCCTCAAACTCCT
THRB
F
GCCTCCAATAGCTCCAGGAT
201
R
CACCCAGTTCCAGGATTCCT
VDR
F
GACGCCCACCATAAGACCTA
247
R
AGATTGGAGAAGCTGGACGA
NR1H2
F
CCTCCTGAAGGCATCCACTA
261
R
GAACTCGAAGATGGGGTTGA
NR1D1
F
AGGCAGCAAGCAAGCAGT
291
R
ACAGCGCATCCTTCCCCATA
NR2C1
F
CCCAAGGCAAGCAGTTCATT
157
R
GCAGACAGATCAGGAGTGGT
NR2C2
F
TCACCACCTCAGACAACCTC
164
R
ACTGACAGCCCCATAGTGAC
NR1I2
F
AACGCAGATGAGGAAGTCGG
103
R
AGCCCTTGCATCCTTCACAT
NR4A3
F
GCCCAATATAGCCCTTCCCC
224
R
TGCATTTGGTACACGCAGGA
NR3C1
F
CTTGCATATTTGTGCCTTCA
174
R
CTTGATGATTTGTGTTGTGC
AR
F
GGGGCTAGACTGCTCAACTG
169
R
GCCAAGTTTTGGCTGAAGAG
NR0B1
F
CAGAGGCCAGGGGGTAAAG
137
R
TGCGCTTGATTTGTGCTCGT
PGR
F
ACATGGTAGCTGTGGGAAGG
198
R
GCTAAGCCAGCAAGAAATGG
RORA
F
TCGAACCAGTAGAAACCGCT
219
R
TTGGCCGAGATGTTGTAGGT
RORB
F
CTCACTTCTCCACCTGCTCA
212
R
GGAGTTGGTGGCTGGGATAT
RORC
F
AGTCGGAAGGCAAGATCAGA
204

R
CAAGAGAGGTTCTGGGCAAG
NR2E3
F
GGAGTCCAACACTGAGTCCC
289
R
GGCCATGAAGAGTAGGCGAG
NR5A1
F
AGGCACCAGGGAAGATCA
241
R
TGCCAGGCCAGGGAATACA
NR2E1
F
CAAGTGGGCTAAGAGTGTGC
158
R
CGTTCATGCCAGATACAGCC
NR4A1
F
GCCAATCTCCTCACTTCCCT
202
R
CAGCAAAGCCAGGGATCTTC
RARA
F
GTGTCACCGGGACAAGAACT
146
R
CGTCAGCGTGTAGCTCTCAG
RXRA
F
CTCTGTTGTGTCCTGTTGCC
155
R
CTTCTCCCTTTGCGTGTTCC
PPARA
F
CTGTCTGCTCTGTGGACTCA
247
R
AGAACTATCCTCGCCGATGG
RARB
F
GGTTTCACTGGCTTGACCAT
216
R
GGCAAAGGTGAACACAAGGT
AHR
F
GGTTTCACTGGCTTGACCAT
274
R
CAGAGGACCAAATCCAGCAT
RARG
F
GAAGACCGCGACACAACTTCC
180
R
GTTGAGTTAAGACATGAGGG
RXRB
F
GCAGGAGTAGGAGCCATCTT
188
R
GCATACACTTTCTCCCGCAG
THRA
F
ACCTCCATCCCACCTATTCC
242
R
CTCTTCAGGAGTGGGCTCTG
NR1H3
F
GAGATCCTCCCGTGGCATTA
151
R
GAGAACCCTGTGCAAAGTGG
F, forward; R, reverse

Table 2 Metadichol and Human Mesenchymal Stem Cells
Metadichol concentrations
1 pg
100 pg
1 ng
100 ng
Common name
Nomenclature name
DAX1
0.19
0.33
0.38
0.11
NR0B1
SHP
1.39
0.75
1.06
0.29
NR0B2
TRα
16.16
12.24
7.7
5.32
NR1A1
TRβ
7.71
1.94
15.11
8.71
NR1A2
RARα
1.27
0.79
0.52
0.44
NR1B1
RARB
1.67
1.39
0.48
0.73
NR1B2
RARγ
2.52
1.04
0.96
0.82
NR1B3
PPARα
4.66
3.48
3.92
1.35
NR1C1
PPAR-β/δ
3.74
4.5
4.5
0.44
NR1C2
PPARG
1.82
1.7
1.03
1
NR1C3
Rev-ErbAα
1.93
1.29
0.8
0.6
NR1D1
Rev-ErbAβ
0.89
0.47
0.35
0.15
NR1D2
RORα
1.77
1.39
0.94
0.67
NR1F1
RORβ
0.81
0.84
0.7
0.33
NR1F2
RORγ
0.52
0.74
1.19
1.08
NR1F3
LXRB
1.28
0.97
0.55
0.19
NR1H2
LXRα
1.28
1.17
0.84
0.18
NR1H3
FXR
1.98
1.09
0.53
0.6
NR1H4
VDR
2.03
0.92
3.67
0.54
NR1I1
PXR
0.6
0.74
1.11
0.39
NR1I2
CAR
8.03
1.49
2.91
10.61
NR1I3
HNF4A
0.99
0.72
0.51
0.13
NR2A1
HNF4γ
1.39
1.51
0.36
0.26
NR2A2
RXRA
1.4
1.21
0.99
0.79
NR2B1
RXRB
1.87
1.13
1.05
0.69
NR2B2
RXRG
2.15
2.2
1.5
0.76
NR2B3
TR2
1.3
1.27
0.74
0.39
NR2C1
TR4
1.6
1.5
0.74
0.51
NR2C2

TLX
0.95
1.37
1.18
0.57
NR2E1
PNR
2.18
1.23
1.51
1.25
NR2E3
COUP-TFI
1.78
1.57
1.04
0.65
NR2F1
COUP-TFII
1.81
1.48
1.15
1.07
NR2F2
EAR-2
0.98
0.95
0.43
0.08
NR2F6
ERα
1.86
1.17
1.94
0.4
NR3A1
ERβ
1.81
1.37
1.09
0.66
NR3A2
ERRα
1
0.88
0.59
0.35
NR3B1
ERRβ
1.11
2.32
1.45
1.36
NR3B2
ERRγ
1.84
1.02
0.55
0.18
NR3B3
GR
0.99
0.86
0.82
0.09
NR3C1
MR
1.15
0.78
0.52
0.21
NR3C2
PR
1.19
0.94
0.67
0.12
NR3C3
AR
1.15
0.37
0.11
0.33
NR3C4
NGFIB
1.82
0.67
1.16
0.61
NR4A1
NURR1
1.06
0.61
0.45
0
NR4A2
NOR1
5.43
1.89
0.35
0.5
NR4A3
SF1
3.2
2.56
1.92
5.09
NR5A1
LRH1
1.3
0.72
0.29
0.15
NR5A2
GCNF
1.7
0.86
0.65
0.15
NR6A1
AHR
0.39
0.58
0.79
0.51
AHR
Table 3 Metadichol and normal human dermal fibroblasts
Metadichol concentrations
1pg
100pg
1ng
100ng
Common Name
Nomenclature
DAX1
1.76
0.14
0.44
0.24
NR0B1
SHP
No
Detectable
expression
No Detectable
expression
No Detectable
expression
No Detectable
expression
NR0B2
TRα
1.2
0.65
0.55
0.69
NR1A1
TRβ
1.15
1.18
2.4
0.92
NR1A2
RARα
2.15
1.81
0.63
1.06
NR1B1

RARB
2.68
1.3
1.18
1.1
NR1B2
RARγ
2.84
2.95
3.9
1.09
NR1B3
PPARα
1.9
1.24
0.72
1.8
NR1C1
PPAR-β/δ
2.48
2.59
2.83
0.84
NR1C2
PPARG
3.78
6.11
7.31
3.07
NR1C3
Rev-ErbAα
1.5
0.64
0.07
0.75
NR1D1
Rev-ErbAβ
2.19
1.18
1.8
1.82
NR1D2
RORα
0.9
1.14
1.33
1.01
NR1F1
RORβ
2.71
0.99
0.69
0.88
NR1F2
RORγ
1.86
1.02
0.91
1.04
NR1F3
LXRB
4.95
1.03
0.03
1.71
NR1H2
LXRα
1.63
1.79
3.84
0.71
NR1H3
FXR
2.69
1.32
1.27
0.87
NR1H4
VDR
1.83
2.34
2.35
1.54
NR1I1
PXR
1.81
0.37
0.97
1
NR1I2
CAR
0.98
0.67
0.49
0.53
NR1I3
HNF4A
6.03
3.4
2.69
3.64
NR2A1
HNF4γ
2.15
1.39
1.2
1.95
NR2A2
RXRA
2.5
0.86
1.32
0.98
NR2B1
RXRB
4.21
1.65
1.03
2.7
NR2B2
RXRG
2.84
2.95
3.9
1.09
NR2B3
TR2
1.08
1.16
1.7
0.85
NR2C1
TR4
3.66
1.38
1.09
4.17
NR2C2
TLX
3.38
1.49
1.69
0.88
NR2E1
PNR
1.43
1.46
2.48
1.27
NR2E3
COUP-TFI
0.16
0.9
4.18
0.5
NR2F1
COUP-TFII
1.05
1.19
2.98
0.59
NR2F2
EAR-2
2.84
1.22
1.66
1.02
NR2F6
ERα
2.42
1.58
0.73
2.02
NR3A1
ERβ
3.04
3.04
1.25
1.52
NR3A2
ERRα
10.31
8.3
6.58
11.1
NR3B1

ERRβ
0.92
0.74
0.48
1.76
NR3B2
ERRγ
0.81
1.07
1.86
0.45
NR3B3
GR
1.71
0.14
0.77
0.45
NR3C1
MR
3.7
0.26
1.04
0.42
NR3C2
PR
0.71
1.25
2.19
0.51
NR3C3
AR
2.54
0.15
0.57
0.11
NR3C4
NGFIB
2.36
1.55
0.93
3.42
NR4A1
NURR1
3.56
6.02
8.63
7.62
NR4A2
NOR1
2.14
0.72
1.48
1.59
NR4A3
SF1
27.27
13.78
13.28
2.69
NR5A1
LRH1
3.32
1.79
1.74
2.02
NR5A2
GCNF
2.46
0.83
0.39
2.19
NR6A1
AHR
10.17
4.32
3.79
1.52
AHR
The highlighted cell in yellow shows the highest expression recorded at 1 picogram/ml.
Discussion
Assuming a threshold of + or - 50% for the fold increase/decrease, we obtained 25 NRs that were unregulated
with stem cells and 36 with fibroblasts at a 1 pg concentration. SF1 (NR5A1) showed an increase of 27-fold in
fibroblasts and 5-fold in stem cells.
NR5A1 guides somatic cells to fetal Sertoli cells (Liang et al., 2019; Li et al., 2013). The latter are vital nurse
cells located in the testis that aim at controlling spermatogenesis and set the immune-privileged environment of
the blood-testis barrier required for the growth of male germ cells. Sertoli cells also secrete cytokines and
growth factors; therefore, they control immune processes that defend germ cells from immunological attack
(Rotgers et al., 2018; Kaur et al., 2014; Kaur et al., 2015). Additionally, they aid in blocking the multiplication
of T, B, and NK cells. These findings are deployed in lessening the immune response in cell transplantation in
diabetes and neurogenerative diseases, skin implants, and other illnesses (Mital et al., 2010). However, defects
in Sertoli cells can cause infertility.
Alternately, Oct4, a pluripotent transcription factor, is involved in the maintenance of self-renewal in stem cells
as well as somatic cells (Luca et al. 2018; Yang et al., 2007). In this context, SF1 is critical in regulating the
transcription of Oct4. However, NR5A2 (LRH-1) can program somatic cells, leading to iPSCs, and thus can
replace Oct4i and result in a better yield of iPSCs (Heng et al., 2010).
We did not detect SHP (NROB2) expressed in fibroblasts and downregulated in stem cells. Huang et al.
provided a possible explanation, which proved that SHP gene transcription is constantly induced by the
expression and activation of nuclear factor-erythroid 2 p45-related factor 2 (NRF2), which encodes NFE2l2
(Heng et al., 2010). In addition, NRF2 is key in controlling the negative effects of electrophilic and oxidative

stress (Xue D et al., 2020) and does so by activating the expression of a wide array of antioxidant response
genes. On the one hand, applying NRF2 activators in clinical trials (Yagishita et al, 2020) could prevent cancer
and treat some diseases related to oxidative stress; on the other hand, integral triggering of NRF2 in many
cancers can lead to the survival and proliferation of tumor cells, as well as resistance to anticancer therapy
(Dornas et al, 2019). In this review, we presented the NRF2 signaling pathway, discussed its role in
carcinogenesis, and introduced the inhibition of NRF2 by NRs. In this context, (Namani et al. 2014) revealed
that the NRF2 signaling pathway can be regulated and inhibited with NRs such as RARα, RXRα, PPARγ, ERα,
ERRβ, and GR (Raghavan, 2022). Since these are expressed by both stem cells and fibroblasts, SHP is expected
to be downregulated in stem cells and not expressed in fibroblasts. The expression of AHR is increased in skin
fibroblasts (10-fold increase) compared to stem cells (60% downregulated). In fact, AHR (Nakatsuji et al, 2019)
is involved in the function of the skin as the first barrier against many pathogens and environmental threats.
Moreover, AHR controls (Roztocil et al 2020) immune-mediated skin reactions and many physiological
functions by acting as a sensor that mediates environment–cell interactions, predominantly during immune and
inflammatory responses. Consequently, AHR plays a crucial role in skin integrity and immunity (Rothhammer
et al 2019) in both homeostasis and disease. This might explain the reason behind the enormous growth of AHR
expression.
To further understand the direct interactions among NRs, we analyzed gene networks using Pathway Studio
(Huang et al., 2010; Fernández-Gallego et al., 2021). Nuclear receptor interaction maps showed the presence of
a feedback-loop network, as shown in Figure 1. The 25 most significantly enriched cell processes regulated by
the gene set (p<E-9) are shown in Table 4. A complete list of processes regulated by the gene subsets is
available in the supplementary material.

Figure 1; Feedback-loop Network Nuclear Receptor Interactions
Table 4; Cell processes regulated by the nuclear receptors
Cell processes
regulated by entities
enriched in the input
Total # of
Neighbors
Gene Set Seed
Overlap
Percent
Overlap
Overlapping Entities
p-value
Protein regulators of
energy homeostasis
1185
energy
homeostasis
39
3
PPARD,RXRG,PPARG,HNF4A,NR2F2,NR1I3
,NR1H4,ESR1,ESR2,ESRRA,NR1D2,ESRRB,
NR5A2,NR3C2,NR0B2,ESRRG,NR4A2,THRB
,VDR,NR1H2,NR1D1,NR2C2,NR1I2,NR4A3,
NR3C1,AR,RORA,RORB,RORC,NR5A1,NR4
A1,RARA,RXRA,PPARA,RARB,AHR,RARG,
THRA,NR1H3
8.3839E-34
Protein regulators of
gene repression
737
gene repression
33
4
PPARD,PPARG,NR2F1,HNF4A,NR2F2,NR1H
4,ESR1,ESR2,NR1D2,NR5A2,NR3C2,NR0B2,
NR2F6,NR6A1,THRB,VDR,NR1D1,NR1I2,N
R3C1,AR,NR0B1,PGR,RORA,NR5A1,NR2E1,
NR4A1,RARA,RXRA,PPARA,RARB,AHR,R
XRB,THRA
1.6368E-31

Protein regulators of
transcription
activation
3198
transcription
activation
48
1
PPARD,PPARG,HNF4A,HNF4G,NR1I3,NR1H
4,ESR1,ESR2,ESRRA,NR1D2,ESRRB,ESRRG
,THRB,NR1H2,NR2C1,NR2C2,NR1I2,NR4A3,
AR,NR0B1,PGR,NR2E3,NR2E1,RXRA,AHR,
RXRB,NR2F1,NR2F2,NR5A2,NR3C2,NR0B2,
NR4A2,NR2F6,NR6A1,VDR,NR1D1,NR3C1,
RORA,RORB,RORC,NR5A1,NR4A1,RARA,P
PARA,RARB,RARG,THRA,NR1H3
9.7744E-31
Protein regulators of
liver metabolism
524
liver metabolism
27
5
PPARD,RXRG,PPARG,HNF4A,NR1I3,NR1H
4,ESR1,ESRRA,NR5A2,NR0B2,ESRRG,THR
B,VDR,NR1H2,NR1D1,NR1I2,NR4A3,NR3C1
,AR,RORA,RORC,NR5A1,NR4A1,RXRA,PPA
RA,AHR,NR1H3
1.4922E-26
Protein regulators of
lipid homeostasis
652
lipid homeostasis
28
4
PPARD,RXRG,PPARG,HNF4A,NR1I3,NR1H
4,ESR1,ESR2,ESRRA,NR1D2,NR5A2,NR3C2,
NR0B2,VDR,NR1H2,NR1D1,NR2C2,NR1I2,N
R3C1,AR,NR0B1,RORA,NR4A1,RXRA,PPAR
A,AHR,THRA,NR1H3
1.8223E-25
Protein regulators of
lipid metabolism
2420
lipid metabolism
41
1
PPARD,RXRG,PPARG,HNF4A,HNF4G,NR2F
2,NR1I3,NR1H4,ESR1,ESR2,ESRRA,NR1D2,
NR5A2,NR3C2,NR0B2,ESRRG,NR6A1,THRB
,VDR,NR1H2,NR1D1,NR2C2,NR1I2,NR4A3,
NR3C1,AR,NR0B1,PGR,RORA,RORB,RORC,
NR5A1,NR4A1,RARA,RXRA,PPARA,AHR,R
ARG,RXRB,THRA,NR1H3
1.1817E-24
Protein regulators of
steroidogenesis
879
steroidogenesis
29
3
PPARD,PPARG,NR2F1,NR2F2,NR1H4,ESR1,
ESR2,ESRRA,NR1D2,ESRRB,NR5A2,NR0B2,
ESRRG,THRB,VDR,NR1H2,NR1D1,NR1I2,N
R3C1,AR,NR0B1,PGR,NR5A1,NR4A1,RXRA,
PPARA,AHR,RXRB,NR1H3
2.9507E-23
Protein regulators of
cholesterol
homeostasis
393
cholesterol
homeostasis
22
5
PPARD,PPARG,NR1I2,HNF4A,HNF4G,NR3C
1,AR,NR1I3,NR1H4,RORA,ESR1,NR1D2,NR
5A2,NR0B2,RXRA,PPARA,THRB,AHR,NR1
H2,NR1D1,RXRB,NR1H3
4.9509E-22
Protein regulators of
gluconeogenesis
1164
gluconeogenesis
30
2
PPARD,PPARG,HNF4A,HNF4G,NR1I3,NR1H
4,ESR1,ESRRA,NR1D2,NR3C2,NR0B2,ESRR
G,VDR,NR1H2,NR1D1,NR2C2,NR1I2,NR4A3
,NR3C1,AR,NR0B1,RORA,RORC,NR5A1,NR
4A1,RARA,PPARA,AHR,THRA,NR1H3
4.2944E-21
Protein regulators of
lipogenesis
1428
lipogenesis
32
2
PPARD,RXRG,PPARG,HNF4A,HNF4G,NR1I
3,NR1H4,ESR1,ESR2,ESRRA,NR1D2,NR5A2,
NR3C2,NR0B2,ESRRG,NR6A1,THRB,VDR,N
R1H2,NR1D1,NR2C2,NR1I2,NR3C1,AR,NR0
B1,RORA,NR4A1,RXRA,PPARA,AHR,RARG
,NR1H3
5.7662E-21
Protein regulators of
fatty acid oxidation
1046
fatty acid
oxidation
28
2
PPARD,PPARG,HNF4A,NR2F2,NR1I3,NR1H
4,ESR1,ESRRA,NR0B2,ESRRG,NR4A2,THR
B,VDR,NR1H2,NR1D1,NR1I2,NR4A3,NR3C1
,RORA,RORC,NR4A1,RXRA,PPARA,RARB,
AHR,RARG,THRA,NR1H3
7.108E-20
Protein regulators of
chromatin remodeling
1626
chromatin
remodeling
32
1
PPARD,RXRG,PPARG,NR2F1,HNF4A,NR1H
4,ESR1,ESR2,ESRRB,NR5A2,NR3C2,NR0B2,
THRB,VDR,NR1D1,NR2C1,NR2C2,NR4A3,N
R3C1,AR,PGR,RORA,RORC,NR5A1,NR4A1,
RARA,RXRA,PPARA,RARB,AHR,THRA,NR
1H3
2.9862E-19

Protein regulators of
fatty acid beta-
oxidation
537
fatty acid beta-
oxidation
22
4
PPARD,RXRG,PPARG,NR1I2,NR2F1,HNF4A
,AR,NR1I3,NR1H4,PGR,ESR1,ESRRA,NR0B
2,ESRRG,NR4A1,RARA,RXRA,PPARA,VDR,
AHR,NR1H2,NR1H3
4.2967E-19
Protein regulators of
adipocyte
differentiation
1455
adipocyte
differentiation
30
2
PPARD,PPARG,NR2F2,NR1H4,ESR1,ESR2,E
SRRA,NR1D2,NR5A2,NR3C2,ESRRG,NR4A2
,NR2F6,VDR,NR1D1,NR4A3,NR3C1,AR,NR0
B1,RORA,RORC,NR4A1,RARA,RXRA,PPAR
A,RARB,AHR,RARG,THRA,NR1H3
2.4971E-18
Protein regulators of
cell development
4071
cell development
43
1
PPARD,PPARG,NR2F1,HNF4A,NR2F2,NR1H
4,ESR1,ESR2,ESRRA,NR1D2,ESRRB,NR5A2
,NR3C2,NR0B2,ESRRG,NR4A2,NR2F6,NR6A
1,THRB,VDR,NR1H2,NR1D1,NR2C2,NR1I2,
NR4A3,NR3C1,AR,NR0B1,PGR,RORA,ROR
B,RORC,NR2E3,NR5A1,NR2E1,NR4A1,RAR
A,RXRA,PPARA,AHR,RARG,THRA,NR1H3
3.6059E-18
Protein regulators of
adipogenesis
2019
adipogenesis
33
1
PPARD,RXRG,PPARG,NR2F1,NR2F2,NR1H4
,ESR1,ESR2,ESRRA,NR5A2,NR3C2,ESRRG,
NR4A2,NR2F6,THRB,VDR,NR1D1,NR2C2,N
R4A3,NR3C1,AR,NR0B1,RORA,RORC,NR4A
1,RARA,RXRA,PPARA,RARB,AHR,RARG,T
HRA,NR1H3
1.5873E-17
Protein regulators of
circadian rhythm
906
circadian rhythm
24
2
PPARD,PPARG,HNF4A,NR1I3,ESR1,ESRRA,
NR1D2,NR3C2,NR0B2,NR4A2,THRB,NR1H2
,NR1D1,NR1I2,NR3C1,RORA,RORB,RORC,
NR2E3,NR4A1,RXRA,PPARA,AHR,NR1H3
1.3695E-16
Protein regulators of
lipid storage
2174
lipid storage
33
1
PPARD,PPARG,HNF4A,NR2F2,NR1I3,NR1H
4,ESR1,ESR2,ESRRA,NR3C2,NR0B2,ESRRG,
NR2F6,NR6A1,VDR,NR1H2,NR1D1,NR2C2,
NR1I2,NR3C1,AR,NR0B1,PGR,RORA,NR5A
1,NR4A1,RARA,RXRA,PPARA,AHR,RXRB,
THRA,NR1H3
1.5312E-16
Protein regulators of
fatty acid metabolism
733
fatty acid
metabolism
21
2
PPARD,RXRG,PPARG,NR1I2,HNF4A,NR4A
3,NR2F2,AR,NR1I3,NR1H4,ESR2,ESRRA,NR
5A2,NR4A2,NR4A1,RXRA,PPARA,VDR,AH
R,NR1H2,NR1H3
4.7993E-15
Protein regulators of
cellular aging
1834
cellular aging
29
1
PPARD,PPARG,HNF4A,NR2F2,NR1H4,ESR1
,ESR2,ESRRA,ESRRB,NR5A2,NR3C2,ESRR
G,NR4A2,THRB,VDR,NR1H2,NR1D1,NR4A3
,NR3C1,AR,PGR,RORA,NR2E1,NR4A1,RAR
A,RXRA,PPARA,RARB,AHR
1.7007E-14
Protein regulators of
cellular senescence
1563
cellular
senescence
27
1
PPARD,PPARG,HNF4A,NR2F2,NR1H4,ESR1
,ESR2,ESRRA,ESRRB,NR5A2,NR3C2,ESRR
G,NR4A2,THRB,VDR,NR1H2,NR1D1,AR,PG
R,RORA,NR2E1,NR4A1,RARA,RXRA,PPAR
A,RARB,AHR
2.9963E-14
We can further refine the process of the 32 NRs involved in chromatin remodeling, which is one of the regulated cell
processes. Figure 2 shows a rigid system of NRs that interrelate in chromatin remodeling. DNA of eukaryotic genomes
complexed with histones forms higher-order chromatin structures, of which the basic unit is the nucleosome involving
∼146 bp of DNA wrapped around a histone octamer (Nikitin et al., 2003). Chromatin has an important role in DNA
replication, repair, cell division, transcription (Sivachenko et al., 2007), and the nuclear receptor-signaling axis. It affects

NR action by specifying its genomic localization and interactions with regulatory elements. Consequently,
chromatin acts as a regulator of selective NR interactions with DNA to drive specific transcriptional programs.
Figure 2; Network of 32 NRs interrelated in chromatin remodeling
To repress or enhance transcription, NRs bind to particular DNA sequences and recruit cofactors that alter the chromatin
structure, which leads to RNA polymerase actions (Hebbar and Archer, 2003; Luger et al., 1997; Lee et al., 1993). The
activity of chromatin remodeling complexes is fundamental to biological processes that include pluripotency and its
maintenance, cellular differentiation, inflammation, DNA damage and repair, and tumor suppression. Therefore,
chromatin is an integral component of the pathways that guide NR action in a cell-type-specific and cell-state-dependent
manner (Zaret and Yamamoto, 1984). Understanding the roles of cofactors and the mechanisms regulating NR actions on
chromatin and transcription could provide original druggable pathways due to the emergence of epigenetic and chromatin
regulators as novel targets in disease treatment.

Conclusions
To the best of our knowledge, this research is the first to show that NRs can be expressed from undifferentiated,
uncommitted, and differentiated NRs. Our procedure is generic and can be applied to study various cells.
Metadichol, is potentially a natural ligand for all 49 NRs. Previously we have shown that it binds to to VDR,
AHR, THRA, THRB, and RORC. (Raghavan, 2015; Raghavan, 2017; Raghavan, 2019;;; Raghavan, 2017-1).
NR’s are differentially expressed in the two groups of cells (stems and fibroblasts. More studies need to be
carried out with different types of cells to better understand the nature of nuclear expression by treatment with
Metadichol. Ongoing research based on this approach will be reported soon.
Declaration of Interest
The author declares no competing interests.
Funding
R&D budget of Nanorx Inc.
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