Introduction
In 2006, Dr. Yamanaka and his group (1) showed that somatic cells could are programmed using just four genes, i.e., Oct4, Sox2, Klf4, and c-Myc (OSKM; also called Yamanaka factors). These genes are used to reprogram adult mouse fibroblasts (connective tissue cells) into an embryonic state known as pluripotency. Pluripotent cells are similar to embryonic stem cells and can become any other cell type in the body.
In 2011, a French researcher, Jean-Marc Lemaitre, added two additional factors (Nanog and LIN28) to the Yamanaka factors and reported comparing aged fibroblasts from healthy older adults and healthy people over 100 years old. These factors lead to cellular rejuvenation. The six factors mentioned above can reprogram cells into induced pluripotent stem cells (iPSCs), which can convert to any other cell type in the body (2). Kromer et al. (3) showed that aged cells such as fibroblasts have short telomeres and dysfunctional mitochondria.
It is relatively easy to isolate cells in a dish, revert them to a developmental state, and then reprogram them into the desired cells using Yamanaka factors. However, this is not practical in living animals because cell memory cannot be erased and reverted to a pluripotent state. It is also possible that the expression of Yamanaka factors causes cancer in animals (4).
Belmonte and his team reported that the cells and organs of living animals could be rejuvenated (5). The research group used a specially engineered mouse strain designed to age more rapidly than usual and an engineered normally aging mouse strain. These mice were engineered to express Yamanaka factors upon exposure to the antibiotic doxycycline in drinking water. Moreover, allowing transient expression of Yamanaka factors for two days > was followed by silencing of these factors upon the removal of doxycycline from the animals’ drinking water.
In late 2020, David Sinclair restored vision in aged mice and mice with damaged retinal nerves using partial cellular reprogramming (6). Only three factors were used, and MYC was removed to reduce cancer risk. This strategy also alleviated age-related vision impairment in treated mice and mice that experienced increased eye pressure, an indicator of glaucoma.
In 2021, researchers exposed cells to OSKM via a doxycycline-inducible lentiviral system, as in previous animal studies (7). This strategy reversed fibroblast cell aging by 30 years, allowing aged cells to function similarly to those in a person approximately 25 years of age.
For the Translation of partial cellular reprogramming strategies in humans, it is necessary to identify a method for inducing Yamanaka factor expression in cells that do not require drugs such as doxycycline.
Other critical challenges related to the clinical application of these cells are their safety and tumorigenic potential when transplanted back into patients (8).
The generation of induced pluripotent stem cells (iPSCs) with viral integration and the use of viruses to deliver transcription factors are associated with tumorigenesis (9). There is a need for reprogramming protocols that do not require permanent genomic integration or viral vectors. Such protocols represent the future of regenerative medicine.
Deng et al. (10) reported that a combination of specific small molecules could reprogram mouse fibroblasts into iPSCs via a single transcription factor, Oct4, eliminating the need for Sox2, Klf4, and c-Myc expression. This finding brought us closer to achieving the generation of iPSCs using small molecules without any genetic modification. Other small molecules that can eliminate the need for exogenous Oct4 expression are needed.
Herein, we show that Metadichol (11), a small nontoxic molecule, naturally induces the expression of Yamanaka factors in vitro in fibroblasts and A549 and Colo-205 cancer cells and primary cancer cell lines.
Experimental procedures – quantitative PCR (qPCR), western blotting, and cell culture
All work was carried out by a service provider, Skanda Life sciences, Bangalore, India.
Chemicals and reagents
A549 cells, Colo-205 cells, and human cardiac fibroblast cells were from the ATCC, USA. Primary breast cancer cells from BIOIVT, Detroit, Michigan, USA. Primary antibodies from either ABclonal, Woburn, Massachusetts, USA, and E-lab science, Maryland, USA. Primers were sourced from SahaGene, Hyderabad, India (Table 1). Other molecular biology reagents were from Sigma Aldrich.
Table 1
1 | OCT4 | F | GTTGATCCTCGGACCTGGCTA | 134 |
R | GGTTGGCTCACTCGGTTCT |
2 | P53 | F | GGCCCACTTCACCGTACTAA | 278 |
R | GTGGTTTCAAGGCCAGATGT |
3 | KLF4 | F | CCCACACAGGTGAGAAACCT | 199 |
R | ATGTGTAAGGCGAGGTGGTC |
4 | SOX2 | F | AACCCCAAGATGCACAACTC | 234 |
R | CGGGGCCGGTATTTATAATC |
5 | c-MYC | F | CCATCCAGGTGAACCACCTA | 241 |
R | ATCTCCGAACACATCACTTC |
6 | NANOG | F | ATCTGCTGGAGGCTGAGGTA | 180 |
R | GTGGTTTCAAGGCCAGATGT |
7 | KLF 2 | F | CTTCTCTCGACGCCATCTCC | 160 |
R | AGCCATCCAAAAGCCCCATT |
8 | KLOTHO | F | AGGGTCCTAGGCTGGAATGT | 158 |
R | CCTCAGGGACACAGGGTTTA |
Maintenance and seeding
The cells were maintained in the appropriate medium, with or without the required supplements and 1% antibiotics, in a humidified atmosphere of 5% CO2 at 37°C. The medium was changed every other day until the cells reached confluency. The viability of the cells was assessed using a hemocytometer.
When the cells reached 70–80% confluence, single-cell suspensions containing 106 cells/mL were prepared and seeded in 6-well plates at a density of 1 million cells per well. The cells were incubated for 24 h at 37°C in 5% CO2. After 24 h, the cell monolayer was rinsed with serum-free medium and treated with Metadichol at predetermined concentrations.
Cell treatments
Different concentrations of Metadichol (1 pg/mL, 100 pg/mL, 1 ng/ml, and 100 ng/mL) were prepared in serum-free medium. Subsequently, a Metadichol-containing medium was added to predesignated wells. Control cells received medium without the drug. The cells were incubated for 24 h. After treatment, the cells were gently rinsed with sterile PBS. Whole-cell RNA was isolated using TRIzol reagent (Invitrogen) per the manufacturer’s instructions, and cDNA was prepared. The samples were analyzed by qPCR and western blotting for various biomarkers.
Quantitative real-time PCR (qRT-PCR)
RNA isolation
Total RNA was isolated from each treatment group using TRIzol. Approximately ~ 1×106 cells were collected in 1.5 mL microcentrifuge tubes. The cells were centrifuged at 5000 rpm for 5 min at four °C, and the cell supernatant was discarded. Then, 650 µL of TRIzol was added to the pellet, and the contents were mixed well and incubated on ice for 20 min. Subsequently, 300 µL of chloroform was added to the mixture, and the samples were mixed well by gentle inversion for 1–2 min and incubated on ice for 10 min. The samples were centrifuged at 12000 rpm for 15 min at four °C. The upper aqueous layer was transferred to a new, sterile 1.5 mL centrifuge tube, and an equal amount of prechilled isopropanol was added to the tube. The samples were incubated at -20°C for 60 min. After incubation, the mixture was centrifuged at 12000 rpm for 15 min at four °C. The supernatant was discarded carefully, and the RNA pellet was retained. The pellet was washed with 1.0 mL of 100% ethanol, followed by 700 µL of 70% ethanol via centrifugation, as described above, after each step. The RNA pellet was air-dried at RT for approximately 15–20 min and then resuspended in 30 µL of DEPC-treated water. The RNA concentration was quantified using a Spectradrop (Spectramax i3x, USA) spectrophotometer (Molecular Devices), and cDNA was synthesized using reverse-transcription PCR (RT-PCR).
cDNA synthesis
cDNA was synthesized from 2 µg of RNA using the PrimeScript cDNA synthesis kit (Takara) and oligo dT primers per the manufacturer’s instructions. The reaction volume was 20 µL, and cDNA synthesis was performed on an Applied Biosystems instrument (Veritii). The cDNA was used for qPCR (50°C for 30 min followed by 85°C for 5 min).
Primers and qPCR
The PCR mixture (final volume of 20 µL) contained 1 µL of cDNA, 10 µL of SYBR green Master Mix, and one µM complementary forward and reverse primers specific for the respective target genes. The samples were run under the following conditions: initial denaturation at 95°C for 5 min, followed by 30 cycles of secondary denaturation at 95°C for 30 s, annealing at the optimized temperature for 30 s, and extension at 72°C for 1 min. The number of cycles that allowed amplification in the exponential range and without reaching a plateau was selected as the optimal number of cycles. The obtained results were analyzed using CFX Maestro software.
Fold change was calculated using the following equation.
(ΔΔCT Method)
The comparative CT method determined the relative expression of target genes to the housekeeping gene (β-actin) and untreated control cells.
Delta CT for each treatment was calculated using the formula.
Delta Ct = Ct (target gene) – Ct (reference gene)
To compare the delta Ct of individually treated samples with the untreated control sample, the Ct was subtracted from the control to get the delta delta CT.
Delta delta Ct = delta Ct (treatment group) – delta Ct (control group)
The fold change in target gene expression for each treatment was calculated using the formula. Fold change = 2^ (− delta delta Ct)
Protein isolation
Total protein was isolated from 106 cells using RIPA buffer supplemented with the protease inhibitor PMSF. The cells were lysed for 30 min at four °C with gentle inversion. The cells were centrifuged at 10,000 rpm for 15 min, and the supernatant was transferred to a fresh tube. The Bradford method determined the protein concentration, and 25 µg of protein was mixed with 1× sample loading dye containing SDS and loaded onto a gel. The proteins were separated under denaturing conditions in Tris-glycine buffer.
The proteins were transferred to methanol-activated PVDF membranes (Invitrogen) using a Turbo transblot system (Bio-Rad, USA). Non-specific binding to the membranes was blocked by incubation in 5% BSA for one h. The membranes were incubated overnight with the respective primary antibody at four °C, followed by a species-specific secondary antibody for one h at RT. The blots were washed and incubated with ECL substrate (Merck) for 1 min in the dark. Images were captured at appropriate exposure settings using a ChemiDoc XRS system (Bio-Rad, USA).
Table 2: RT-PCR results of Metadichol treatment with human cardiac fibroblasts (HCFs)
Summary of results: qRT-PCR
Table 2: RT-PCR results of Metadichol treatment with human cardiac fibroblasts (HCFs)
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)
Table 3
RT-PCR results following Metadichol treatment in A-549 and COLO205 cancer cells
Cancer cell lines (Fold Increase) |
A549 lung carcinoma cell line | P53 | Oct4 (Pou5F1) | Klf4 | Nanog | SOX2 | Klotho |
Concentration of Metadichol | Fold increase | Fold increase | Fold increase | Fold increase | Fold increase | Fold increase |
Control | 1 | 1 | 1 | 1 | 1 | 1 |
1 picogram | No expression | 0.27 | 1.05 | 0.82 | 0.47 | 0.37 |
100 picograms | No expression | 0.92 | 0.44 | 0.82 | 0.80 | 0.60 |
1 nanogram | No expression | 4.01 | 3.51 | 1.26 | 2.51 | 1.99 |
100 nanograms | No expression | 1.25 | 1.58 | 0.81 | 0.73 | 0.83 |
Colo-205 pancreatic cancer cell line | | | | | | |
Concentration of Metadichol | Fold increase | Fold increase | Fold increase | Fold increase | Fold increase | Fold increase |
Control | 1 | 1 | 1 | 1 | 1 | 1 |
1 picogram | No expression | 0.41 | 2.55 | 1.74 | 2.16 | 1.89 |
100 picograms | No expression | 0.88 | 2.98 | 0.68 | 2.21 | 1.22 |
1 nanogram | No expression | 1.79 | 13.17 | 0.41 | 2.25 | 2.58 |
100 nanograms | No expression | 0.81 | 6.77 | 1.19 | 1.71 | 0.86 |
Table 4
RT-PCR results following Metadichol treatment in primary cancer cells (triple-negative primary breast cancer (HCAF-TNPBC) cells)
Human cancer associated fibroblasts (HCAF) triple negative primary breast cancer (TNPBC | c-MYC | Oct4 (Pou5F1) | Klf4 | SOX2 | Klotho |
Concentration of Metadichol | Fold increase | Fold increase | Fold increase | Fold increase | Fold increase |
Control | 1 | 1 | 1 | 1 | 1 |
1 picogram | 3.36 | 4.56 | 3.36 | 0.57 | 2.13 |
100 picograms | 4.39 | 4.94 | 4.39 | 0.62 | 1.10 |
1 nanogram | 4.04 | 19.63 | 8.07 | 2.45 | 0.64 |
100 nanograms | 6.91 | 9.32 | 6.91 | 1.16 | 1.02 |